Python Interview Questions: Ultimate Guide with Answers

Data structures are the backbone of Python programming. Interviewers often ask core python interview questions on lists, tuples, dictionaries, sets, and choosing the right structure for problem-solving. Understanding mutability, efficiency, and practical use cases is essential.

Lists and Tuples – Differences, Use Cases, and Tricky Questions

Lists and tuples are fundamental in Python. Lists are mutable, while tuples are immutable. Knowing when to use which is key in python basic interview questions.

What is the difference between a list and a tuple?

Lists are mutable; elements can be changed. Tuples are immutable; their elements cannot be modified. Tuples are faster and can be used as dictionary keys:

my_list = [1, 2, 3]
my_tuple = (1, 2, 3)

How do you add an element to a list?

Use append() or insert():

my_list = [1, 2]
my_list.append(3)
my_list.insert(1, 5)
print(my_list)

Can a tuple contain a list?

Yes, tuples can hold mutable elements like lists:

my_tuple = (1, [2, 3])
my_tuple[1].append(4)
print(my_tuple)

How do you remove an element from a list?

Use remove() or pop():

my_list = [1, 2, 3]
my_list.remove(2)
print(my_list) # Output: [1, 3]

How can you convert a tuple to a list?

Use list() function:

my_tuple = (1, 2, 3)
my_list = list(my_tuple)
print(my_list)  # Output: [1, 2, 3]

Dictionaries and Sets – Real Interview Scenarios

Dictionaries store key-value pairs, while sets store unique elements. Python interview questions for freshers often test operations, lookups, and practical applications.

How do you create a dictionary?

Use curly braces with key-value pairs:

my_dict = {"name": "Deepthi", "age": 24}
print(my_dict["name"]) # Output: Deepthi

How do you add or update a dictionary item?

Assign a value to a key:

my_dict["city"] = "NY"
# Add 
my_dict["age"] = 26 # Update

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

Sets store unordered, unique elements. Unlike lists, duplicates are automatically removed:

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

How do you remove elements from a set?

Use remove() or discard():

my_set = {1, 2, 3}
my_set.discard(2)
print(my_set) # Output: {1, 3}

How do you get all keys or values from a dictionary?

Use keys() and values():

my_dict = {"a": 1, "b": 2}
print(my_dict.keys())
print(my_dict.values()) # dict_values([1, 2])

Mutable vs Immutable Objects with Practical Examples

Understanding mutability is crucial for python programming interview questions and answers, as it affects memory management and function behavior.

What are mutable objects in Python?

Mutable objects can be changed after creation, e.g., lists and dictionaries:

# Mutable objects can be changed after creation
# Lists are mutable
my_list = [1, 2]
my_list.append(3)  # Modifies the same list
print(my_list)     # Output: [1, 2, 3]

What are immutable objects?

Immutable objects cannot be changed once created, e.g., strings, tuples, and numbers:

# Immutable objects cannot be changed after creation
# Strings are immutable
x = "hello"
x.replace("h", "H")  # Does NOT change the original string
print(x)             # Output: hello

How does mutability affect function arguments?

Mutable objects passed to a function can be modified inside the function, whereas immutable objects cannot:

# Mutability effect on function arguments
# Mutable objects can be modified inside a function
def add_item(lst):
    lst.append(5)  # Modifies the original list

my_list = [1, 2]
add_item(my_list)
print(my_list)       # Output: [1, 2, 5]

Can you make an immutable list?

Use a tuple to store immutable sequences:

# Making an immutable list
# Tuples are immutable and can be used instead of lists
immutable_list = (1, 2, 3)
print(immutable_list)

How does Python handle string immutability?

Strings cannot be modified in place. Any operation creates a new string:

# String immutability in Python
# Any string operation creates a new string
s = "abc"
s = s.upper()  # Creates a new string and reassigns it
print(s)       # Output: ABC

Choosing the Right Data Structure in Problem-Solving

Selecting the correct data structure is critical for efficiency in python programming interview questions for experienced. Lists, tuples, dictionaries, and sets have distinct trade-offs.

When would you use a list over a tuple?

Use a list when you need a mutable sequence for adding, removing, or updating elements. Tuples are better for fixed collections.

# Using a list when mutability is needed
my_list = [1, 2, 3]
my_list.append(4)  # Adding element
print(my_list)

When is a set preferred?

Sets are ideal when uniqueness matters or for fast membership testing:

# Using a set when uniqueness or fast membership testing is needed
nums = {1, 2, 3, 3}  # Duplicate 3 is removed automatically
print(nums)  # Output: {1, 2, 3}

Why choose a dictionary over a list?

Dictionaries provide O(1) average lookup and are perfect for key-value mappings, whereas list searches are O(n).

# Using a dictionary for key-value mapping
my_dict = {"name": "Shekar", "age": 50}
print(my_dict["name"])  # Output: Shekar

Can you combine different data structures?

Yes, complex structures like dictionaries of lists are common:

# Combining different data structures (dictionary of lists)
students = {"classA": ["Shekar", "Laxmi"], "classB": ["Varun"]}
print(students)

How do you decide based on time complexity?

Use lists for indexed access, sets for uniqueness, dictionaries for key-value lookup, and tuples when immutability and memory efficiency are needed.

Control flow is the foundation of writing functional Python programs. Interviewers often test your understanding of conditional statements, loops, and control keywords to evaluate logical thinking and problem-solving skills. Writing clean, bug-free logic is crucial in python programming questions for interview.

Conditional Statements and Nested Conditions

Conditional statements control the flow of execution based on specific conditions. Understanding if, elif, and else is a common topic in python basic interview questions.

What is the syntax of an if statement in Python?

An if statement evaluates a condition and executes the block if true:

# If statement example
x = 10
if x > 5:
    print("x is greater than 5")

How does an elif statement work?

elif allows checking multiple conditions sequentially after if:

# Elif statement example
x = 15
if x < 10:
    print("Small")
elif x < 20:
    print("Medium")
else:
    print("Large")

Can if statements be nested in Python?

Yes, you can nest if statements to handle complex logic:

# Nested if statement example
x = 12
if x > 10:
    if x % 2 == 0:
        print("Even and greater than 10")

How do you write a one-line if statement?

Python allows a simple inline if statement:

# One-line if statement
x = 5
print("Positive") if x > 0 else print("Non-positive")

What happens if no else is provided?

If no else is present and the condition is false, Python simply skips the block without an error.

Looping Concepts and Common Mistakes

Loops are essential for repeating tasks. Python questions and answers often focus on for and while loops, off-by-one errors, and infinite loops.

What is a for loop in Python?

A for loop iterates over a sequence of items:

# For loop example
for i in range(5):
    print(i)

How is a while loop different from a for loop?

A while loop runs as long as a condition is true:

# While loop example
i = 0
while i < 5:
    print(i)
    i += 1

What is an infinite loop, and how can you avoid it?

An infinite loop occurs when the termination condition is never met. Always ensure the loop variable updates correctly.

Can loops have an else clause?

Yes, the else block executes if the loop completes without a break:

# For loop with else
for i in range(3):
    print(i)
else:
    print("Loop completed")

Common mistakes in loops?

Off-by-one errors, modifying the sequence inside the loop, or infinite loops are common pitfalls in Python interviews.

Break, Continue, and Pass in Real Code Examples

Control keywords modify the flow inside loops. These are frequently asked in python technical interview questions.

How does break work?

Break terminates the loop immediately:

# Break example
for i in range(5):
    if i == 3:
        break
    print(i)  # Output: 0, 1, 2

How does continue work?

Continue skips the current iteration and moves to the next:

# Continue example
for i in range(5):
    if i == 2:
        continue
    print(i)  # Output: 0, 1, 3, 4

What is the use of pass?

Pass is a placeholder and does nothing. Useful in empty blocks:

# Pass example
for i in range(3):
    pass  # Placeholder, does nothing

Can break and continue be used in while loops?

Yes, they work in both for and while loops to control flow dynamically.

Give an example using all three keywords.

# Using break, continue, and pass together
for i in range(5):
    if i == 1:
        continue
    elif i == 3:
        break
    else:
        pass
    print(i)  # Output: 0, 2

Writing Clean Logic Under Time Pressure

Interviewers assess your ability to solve problems quickly and clearly. Python scenario based interview questions often involve loops, conditions, and string or number manipulation.

How do you reverse a string in Python?

Using slicing:

# Reverse a string
s = "Python"
print(s[::-1])  # Output: nohtyP

How do you find even numbers in a list?

Use a loop or list comprehension:

# Find even numbers in a list using list comprehension
nums = [1, 2, 3, 4]
evens = [x for x in nums if x % 2 == 0]
print(evens)  # Output: [2, 4]

How to check for a prime number?

Loop from 2 to n-1 and check divisibility:

# Check for a prime number
n = 7
is_prime = all(n % i != 0 for i in range(2, n))
print(is_prime)  # True

How to sum numbers in a list?

Use sum() or loop:

# Sum numbers in a list
nums = [1, 2, 3]
print(sum(nums))  # Output: 6

How to flatten a nested list?

Using list comprehension:

# Flatten a nested list using list comprehension
nested = [[1, 2], [3, 4]]
flat = [x for sub in nested for x in sub]
print(flat)  # Output: [1, 2, 3, 4]

Functions are the building blocks of modular Python programs. Interviewers often test your ability to define, call, and manage functions efficiently, along with argument handling, lambda functions, and scope understanding.

Function Definition, Parameters, and Return Values

What is a function in Python?

A function is a reusable block of code that performs a specific task. Functions help organize code and reduce redundancy. You define a function using def and optionally return values using return.

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

# Function definition and return value
def greet(name):
    return f"Hello, {name}"

print(greet("Varun"))  # Output: Hello, Varun

How do you pass parameters to a function?

Parameters can be passed by position (positional arguments) or by name (keyword arguments). Python functions can also accept variable-length arguments.

# Positional arguments
def add(a, b):
    return a + b

print(add(5, 3))  # Output: 8

# Keyword arguments
def greet_person(name, age):
    print(f"{name} is {age} years old")

greet_person(age=25, name="Varun")  # Order can change

Can a function return multiple values?

Yes, functions can return multiple values as a tuple:

def get_info():
    return "Varun", 25

name, age = get_info()
print(name, age)  # Output: Varun 25

What is a default parameter?

Default parameters provide a fallback value if no argument is passed:

def greet(name="User"):
    return f"Hello, {name}"

print(greet())  # Output: Hello, User
print(greet("Shekar"))  # Output: Hello, Shekar

Can a function call another function?

Yes, functions can call other functions within the same program, promoting modularity and reuse.

def square(x):
    return x * x

def double_square(x):
    return 2 * square(x)

print(double_square(3))  # Output: 18

Default, Keyword, and Variable-Length Arguments

What are keyword arguments in Python?

Keyword arguments are passed by name instead of position. They improve readability and allow argument order flexibility.

def greet(name, age):
    print(f"{name} is {age} years old")

greet(age=25, name="Varun")

What is the use of *args?

*args allows passing a variable number of positional arguments to a function.

# *args example
def add(*args):
    return sum(args)

print(add(1, 2, 3, 4))  # Output: 10

What is the use of **kwargs?

**kwargs allows passing a variable number of keyword arguments as a dictionary.

# **kwargs example
def info(**kwargs):
    print(kwargs)

info(name="Varun", age=25)  # Output: {'name': 'Varun', 'age': 25}

Can you mix positional, default, and keyword arguments?

Yes, but the order must be: positional, default, *args, and **kwargs. Python enforces this for clarity.

Why use variable-length arguments?

They make functions flexible, allowing dynamic input without rewriting the function for every use case.

Lambda Functions and Their Interview Use Cases

What is a lambda function?

Lambda functions are small, anonymous functions defined with lambda. They are useful for short operations, like sorting or mapping, where defining a full function is unnecessary.

square = lambda x: x*x
print(square(5))  # Output: 25

Can lambda functions have multiple arguments?

Yes, a lambda can take multiple arguments but only one expression:

add = lambda x, y: x + y
print(add(3, 4))  # Output: 7

Where are lambda functions commonly used?

Lambda functions are often used in map(), filter(), sorted(), and reduce() for concise, inline operations.

nums = [1, 2, 3, 4]
squared = list(map(lambda x: x*x, nums))
print(squared)  # Output: [1, 4, 9, 16]

Can a lambda function return a list?

Yes, it can return any Python object, including lists:

get_list = lambda x: [i*i for i in range(x)]
print(get_list(4))  # Output: [0, 1, 4, 9]

Can a lambda function replace a normal function?

For simple, one-line logic, yes. For complex logic, normal functions are preferred for readability and maintainability.

# Simple lambda
double = lambda x: 2*x

# Complex function (better with def)
def process_list(lst):
    return [x*x for x in lst if x % 2 == 0]

Scope, Lifetime of Variables, and Common Confusions

What is the scope of a variable?

Scope determines where a variable can be accessed. Global variables are accessible everywhere; local variables exist only inside the function.

x = 10  # Global variable

def func():
    y = 5  # Local variable
    print(x)  # Access global variable inside the function
    print(y)  # Access local variable inside the function

func()
# Output:
# 10
# 5

What happens if a local variable has the same name as a global variable?

The local variable shadows the global variable inside the function. The global variable remains unchanged outside the function.

x = 10  # Global variable

def func():
    x = 5  # Local variable with the same name as global variable
    print(x)  # Local variable 'x' is used

func()
print(x)  # Global variable 'x' remains unchanged
# Output:
# 5  # Local x in func()
# 10  # Global x

What is the global keyword?

The global allows modifying a global variable inside a function.

x = 5  # Global variable

def change():
    global x  # Declare x as a global variable inside the function
    x = 10  # Modify the global variable

change()
print(x)  # Output: 10

What is the nonlocal keyword?

The nonlocal modifies a variable in the nearest enclosing scope, useful for nested functions.

def outer():
    x = 5  # Enclosing scope variable

    def inner():
        nonlocal x  # Modify the variable in the enclosing scope
        x = 10  # Change x in the outer function

    inner()
    print(x)  # Output: 10

outer()

Why is understanding scope important in interviews?

Scope affects variable access, memory, and bug prevention. Interviewers often test it through nested functions or mutable objects.

Key Takeaways So Far

• Namespace management prevents naming conflicts.
• LEGB rule is fundamental for variable resolution.
• Use global and nonlocal with care.

OOP is widely used in Python for writing reusable, modular, and scalable code. Interviews often focus on classes, objects, inheritance, polymorphism, encapsulation, and constructors.

What is a class in Python?

A class is a blueprint for creating objects. It defines attributes (variables) and methods (functions) for objects.

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

# Creating an object (instance) of the class
p = Person("Alice", 25)
print(p.name)  # Output: Alice
print(p.age)   # Output: 25

What is an object?

An object is an instance of a class with specific data. You create objects to use the class blueprint:

# Creating an object of Person class
p = Person("Alice", 25)
print(p.name)  # Output: Alice

What is inheritance in Python?

Inheritance allows one class (child) to inherit attributes and methods from another (parent), promoting code reuse.

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

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

class Employee(Person):  # Employee inherits from Person
    def __init__(self, name, age, emp_id):
        super().__init__(name, age)  # Call the parent class constructor
        self.emp_id = emp_id

    def display_employee_info(self):
        return f"Employee ID: {self.emp_id}"

# Creating an object of Employee
e = Employee("Alice", 25, "E123")
print(e.greet())  # Output: Hello, my name is Alice.
print(e.display_employee_info())  # Output: Employee ID: E123

What is polymorphism?

Polymorphism allows objects of different classes to be treated similarly. It includes method overriding and operator overloading.

class Animal:
    def sound(self):
        return "Some generic animal sound"

class Dog(Animal):
    def sound(self):
        return "Woof!"

class Cat(Animal):
    def sound(self):
        return "Meow!"

# Polymorphism in action
animals = [Dog(), Cat()]
for animal in animals:
    print(animal.sound())  # Outputs: Woof! Meow!

What are encapsulation and abstraction?

Encapsulation hides internal details using private variables. Abstraction exposes only relevant details using methods. Both improve code security and readability.

class Person:
    def __init__(self, name, age):
        self.name = name
        self._age = age  # Protected attribute (indicated by underscore)

    def get_age(self):  # Getter method
        return self._age

    def set_age(self, age):  # Setter method
        if age > 0:
            self._age = age
        else:
            print("Age must be positive.")

# Creating an object of Person class
p = Person("Alice", 25)
print(p.get_age())  # Accessing age through getter method

p.set_age(30)  # Modifying age through setter method
print(p.get_age())  # Output: 30

Advanced topics test deeper Python knowledge like decorators, generators, iterators, memory management, and context managers.

What is a decorator?

A decorator is a function that modifies another function’s behavior without changing its code. Useful for logging, timing, or authentication.

def decorator(func): def wrapper(): print("Before function") func() return wrapper @decorator def say_hello(): print("Hello") say_hello()

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

@decorator  # Applying the decorator
def say_hello():
    print("Hello")

say_hello()

Output

Before function
Hello
After function

What are generators?

Generators yield values lazily using yield, saving memory when processing large sequences.

def gen(): for i in range(3): yield i for val in gen(): print(val)

def gen():
    for i in range(3):
        yield i

for val in gen():
    print(val)

Output

0
1
2

‍

Difference between shallow copy and deep copy?

Shallow copy copies references; changes affect both objects. Deep copy creates independent copies using copy.deepcopy().

import copy
list1 = [[1,2], [3,4]]
list2 = copy.copy(list1)  # Shallow copy
list2[0][0] = 99
print(list1)  # Output: [[99, 2], [3, 4]]
#deepcopy
list3 = copy.deepcopy(list1)
list3[0][0] = 1
print(list1)  # Original remains unchanged: [[99, 2], [3, 4]]

‍

What are context managers?

Context managers handle setup and cleanup automatically using with statements, e.g., file handling:

with open("file.txt") as f:
    data = f.read()
# File is automatically closed here

Why use iterators?

Iterators allow sequential access to elements without storing the entire collection in memory, enhancing efficiency.

Error handling ensures program stability. Python interviews often focus on try-except blocks, custom exceptions, and debugging.

What are exceptions?

Exceptions are runtime errors that disrupt normal program flow. Examples include ZeroDivisionError, ValueError, and FileNotFoundError.

How to handle exceptions?

Use try-except blocks to catch and manage errors gracefully.

try:
    x = 5 / 0
except ZeroDivisionError:
    print("Cannot divide by zero")

What is the else block in exceptions?

Else runs if no exceptions occur.

try:
    x = 10 / 2
except ZeroDivisionError:
    print("Error")
else:
    print("Success")  # Runs if no exception

How do you create custom exceptions?

Inherit from Exception class to define a custom error:

class MyError(Exception):
    pass

def test(num):
    if num < 0:
        raise MyError("Negative numbers are not allowed")

test(-5)  # Raises MyError

How do you debug Python code during interviews?

Use print statements, IDE debugging tools, or Python’s pdb module to step through code and identify issues.

python -m pdb script.py

Handling files and I/O operations is commonly asked in python interview questions for experienced.

How do you read a file safely?

Use the with statement to open files; it automatically closes the file:

with open("data.txt") as f: content = f.read()

with open("data.txt") as f:
    content = f.read()

How to write to a file?

Use write mode 'w' or append mode 'a':

with open("data.txt", "w") as f:
    f.write("Hello Python")

How to handle large files?

Read files line by line or in chunks using readline() or iterators to save memory.

with open("data.txt") as f:
    for line in f:
        print(line.strip())

How to work with CSV and JSON?

Use csv and json modules for structured data:

import csv, json

# CSV example
with open("data.csv") as f:
    reader = csv.reader(f)
    for row in reader:
        print(row)

# JSON example
data = '{"name": "Alice", "age": 25}'
parsed = json.loads(data)
print(parsed['name'])

What are file modes in Python?

Modes include 'r' (read), 'w' (write), 'a' (append), and 'rb'/'wb' for binary files.

Python libraries are critical for specialized roles like data analysis, web development, and automation.

Some commonly used libraries are NumPy, Pandas, Matplotlib, Seaborn for data; Django, Flask for web; Selenium, PyAutoGUI for automation.

How is NumPy used?

NumPy provides fast array operations and numerical computing:

import numpy as np

# Create a NumPy array
arr = np.array([1, 2, 3])

# Element-wise operations
print(arr * 2)  # Output: [2 4 6]

# Useful operations
print(arr + 5)       # [6 7 8]
print(arr.mean())    # 2.0
print(arr.sum())     # 6

How is Pandas useful?

Pandas simplifies data manipulation using DataFrame and Series for analysis, cleaning, and transformation.

What is Django?

Django is a Python web framework used to build scalable, secure web applications with MVC architecture.

How do you use Matplotlib?

Matplotlib is used for data visualization:

import matplotlib.pyplot as plt

# Simple line plot
x = [1, 2, 3]
y = [4, 5, 6]
plt.plot(x, y, marker='o', linestyle='--', color='r')
plt.title("Line Plot Example")
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.show()

How to write efficient Python code?

Use built-in functions, list comprehensions, avoid unnecessary loops, and choose the right data structures for better performance.

What is Big O notation?

Big O describes the worst-case time complexity of an algorithm, helping analyze efficiency.

How does Python handle memory management?

Python uses automatic garbage collection and reference counting to free memory from unused objects.

How to optimize loops in Python?

Use list comprehensions, generator expressions, and avoid nested loops when possible.

What are common performance mistakes?

Using inefficient data structures, unnecessary computations, or storing large data in memory instead of iterating.

How to explain past Python projects?

Clearly describe your project goals, your role, technologies used, challenges faced, and outcomes. Use concise language.

How to handle real-time bugs?

Demonstrate logical debugging, root cause analysis, and corrective measures taken, emphasizing problem-solving skills.

How to make decisions using Python?

Explain code-driven decisions using data or logic, e.g., choosing a dictionary for fast lookups instead of a list.

How to communicate thought process?

Explain your approach step by step, consider edge cases, and discuss alternatives during coding rounds.

How to showcase teamwork in coding tasks?

Highlight collaboration, code reviews, version control usage, and how you contributed to solving complex problems.

How to clean data using Python?

Use Pandas functions like dropna(), fillna(), and apply() to remove or transform missing or inconsistent data.

How to handle large datasets?

Use chunking, generators, or optimized libraries like Dask or Pandas to process large files efficiently.

How to perform statistical analysis?

Use NumPy and SciPy for mean, median, variance, correlation, and other statistics in Python.

How to evaluate models?

Use metrics like accuracy, precision, recall, F1-score, or confusion matrix depending on the problem type.

How to visualize data?

Use Matplotlib, Seaborn, or Plotly for charts, histograms, and trend visualizations.

Mistakes during python interviews are unavoidable even for the most experienced candidates. The majority of mistakes can be traced back to basic misunderstandings, complicated solutions, or miscommunication of reasoning. Early awareness of these traps allows you to show your knowledge in a confident and efficient way.

• Conceptual Gaps: A weak grasp of the Python data types, OOP, or core functions can make you lose the opportunity.
• Overcomplicating Simple Problems: Don't use unnecessary logic; just start with a simple and readable solution.
• Poor Code Explanation: Always clarify your method and logic while programming.
• Ignoring Edge Cases: When you solve problems, think of empty lists, null values, and unexpected input.
• Lack of Practice: Not practicing enough can cause one to be slow in coding, make mistakes, and lose time.

A methodical way of handling the situation is to mix theory, coding practice, and problem-solving. The key is to get the python interview questions for freshers or the python interview questions for experienced candidates, to the point of consistent practicing and understanding the core concepts.

• Get the basics right: Pay attention to Python syntax, data types, loops, functions, and OOP concepts.
• Do some coding practice: Try out problems on sites such as LeetCode, HackerRank, or GeeksforGeeks.
• Familiarize with Libraries: Be at ease with NumPy, Pandas, Matplotlib, Django/Flask, depending on the position.
• Go over Concepts: Go through typical python programming questions and edge cases.
• Mock Interviews: Conduct real interview rounds to increase speed, logic, and communication.

Mastering Python interviews requires a balanced approach: understanding core concepts, practicing problem-solving, and articulating solutions clearly. Whether you are a fresher or experienced, focus on data structures, OOP, functions, and libraries, along with coding exercises and behavioral scenarios to excel in interviews.

Why It Matters?

Nailing Python interviews is about more than just code—it’s about clear thinking, effective communication, and demonstrating best practices that employers value.

Practical Advice for Learners

• Revisit the fundamentals of Python and its advanced concepts from time to time.
• Engage in solving coding challenges on actual platforms.
• Get acquainted with the libraries that are important for your preferred job role.
• Conduct mock interviews for faster and clearer communication.
• Give priority to code clearness and most excellent practices.
• Do not hesitate to articulate your reasoning, particularly during the behavioral rounds.