Python Interview Questions for Freshers (2026): 60+ Questions with Answers, Coding Problems and Output Puzzles

What Python questions are asked to freshers? Almost every fresher interview covers the same core: data types (list, tuple, set, dictionary), mutable vs immutable, functions (*args, **kwargs, lambda), OOP, exception handling, file handling, list comprehensions, and a few coding problems such as reversing a string, finding duplicates or checking a palindrome. For data roles, expect basic Pandas and NumPy questions too.

This guide from VibrantMinds Technologies in Pune covers 60+ Python interview questions for freshers with short answers and runnable code, plus output-prediction puzzles, 15 coding problems with solutions, a revision cheat sheet and a 14-day preparation plan. If you’ve been searching for Python interview questions for freshers, Python interview questions and answers, Python coding interview questions for freshers, Python OOP interview questions, Pandas and NumPy interview questions for freshers, Python developer fresher jobs in Pune or free IT job WhatsApp groups for freshers in Pune, this is built to be the complete answer.

Code in this guide is written for Python 3. Behaviour of some features differs between versions, so check your own version with python --version.

HOW THE PYTHON INTERVIEW USUALLY FLOWS

For Python developer roles, expect more on OOP, functions and frameworks (such as Django or Flask). For Python and Data Science roles, expect Pandas, NumPy, data cleaning and basic statistics along with core Python.

WHAT THE TOP SEARCH RESULTS COVER, AND WHAT THIS GUIDE ADDS

Pages ranking for these searches mostly list 50 to 130 questions with short definitions. This guide keeps the coverage and adds:

• Output-prediction puzzles with explanations, which many tests use

• 15 coding problems with solutions and complexity notes

• A traps column (common mistakes interviewers notice)

• A data science section (Pandas and NumPy) for Python + Data Science freshers

• A one-page cheat sheet and a 14-day plan

• Advice on how to answer and what to say about projects

ONE-PAGE PYTHON CHEAT SHEET (REVISE THIS FIRST)

MODULE 1: PYTHON BASICS (Q1 TO Q12)

Q1. What is Python and why is it popular?
Python is a high-level, general-purpose programming language known for readable syntax. It’s widely used in web development, automation, data science, machine learning and scripting, and it has a large library ecosystem.

Q2. Is Python compiled or interpreted?
In the standard implementation (CPython), source code is first compiled to bytecode and then executed by the Python virtual machine. It’s commonly described as interpreted.

Q3. What does “dynamically typed” mean?
You don’t declare a variable’s type. The type is determined at runtime from the value, and a variable can later refer to a different type.

python

x = 10        # int
x = "hello"   # now str

x = 10        # int
x = "hello"   # now str

x = 10        # int
x = "hello"   # now str

Q4. What is PEP 8?
Python’s style guide for writing readable code: 4-space indentation, naming conventions (snake_case for functions and variables, PascalCase for classes), line length guidance and so on.

Q5. How is Python different from Java?

Q6. What are the built-in data types?
Numeric (int, float, complex), bool, str, list, tuple, set, frozenset, dict, bytes and NoneType.

Q7. What is the difference between == and is?
== compares values. is compares identity (whether two names refer to the same object).

python

a = [1, 2]
b = [1, 2]
print(a == b)   # True
print(a is b)   # False

a = [1, 2]
b = [1, 2]
print(a == b)   # True
print(a is b)   # False

a = [1, 2]
b = [1, 2]
print(a == b)   # True
print(a is b)   # False

Q8. What are mutable and immutable types? Examples?
Mutable objects can change after creation (list, dict, set). Immutable objects cannot (int, str, tuple).

Q9. What is indentation and why does it matter?
Python uses indentation to define code blocks. Incorrect indentation raises an IndentationError or changes program behaviour.

Q10. What is None?
A special object representing the absence of a value. Compare it using is None.

Q11. How do you take input and print output?

python

name = input("Enter your name: ")   # input() returns a string
age = int(input("Enter age: "))
print(f"Hello {name}, you are {age}")

name = input("Enter your name: ")   # input() returns a string
age = int(input("Enter age: "))
print(f"Hello {name}, you are {age}")

name = input("Enter your name: ")   # input() returns a string
age = int(input("Enter age: "))
print(f"Hello {name}, you are {age}")

Q12. What are f-strings?
A concise way to embed expressions in strings (Python 3.6 and later): f"Total: {price * qty}".

Trap: forgetting that input() returns a string, so arithmetic fails without conversion.

MODULE 2: STRINGS, LISTS, TUPLES, SETS, DICTIONARIES (Q13 TO Q26)

Q13. How do you reverse a string?

python

s = "python"
print(s[::-1])       # nohtyp

s = "python"
print(s[::-1])       # nohtyp

s = "python"
print(s[::-1])       # nohtyp

Q14. What is slicing?
Syntax seq[start:stop:step]. The stop index is excluded.

python

nums = [10, 20, 30, 40, 50]
print(nums[1:4])     # [20, 30, 40]
print(nums[::-1])    # [50, 40, 30, 20, 10]

nums = [10, 20, 30, 40, 50]
print(nums[1:4])     # [20, 30, 40]
print(nums[::-1])    # [50, 40, 30, 20, 10]

nums = [10, 20, 30, 40, 50]
print(nums[1:4])     # [20, 30, 40]
print(nums[::-1])    # [50, 40, 30, 20, 10]

Q15. Difference between list and tuple?

Q16. Difference between list, set and dictionary?
A list is an ordered collection allowing duplicates. A set is a collection of unique items. A dictionary stores key-value pairs with unique keys.

Q17. What is a list comprehension? Give an example.

python

squares = [x * x for x in range(1, 6)]            # [1, 4, 9, 16, 25]
evens = [x for x in range(10) if x % 2 == 0]      # [0, 2, 4, 6, 8]

squares = [x * x for x in range(1, 6)]            # [1, 4, 9, 16, 25]
evens = [x for x in range(10) if x % 2 == 0]      # [0, 2, 4, 6, 8]

squares = [x * x for x in range(1, 6)]            # [1, 4, 9, 16, 25]
evens = [x for x in range(10) if x % 2 == 0]      # [0, 2, 4, 6, 8]

Q18. What are dictionary and set comprehensions?

python

sq = {x: x * x for x in range(1, 4)}    # {1: 1, 2: 4, 3: 9}
unique_lengths = {len(w) for w in ["a", "bb", "cc"]}   # {1, 2}

sq = {x: x * x for x in range(1, 4)}    # {1: 1, 2: 4, 3: 9}
unique_lengths = {len(w) for w in ["a", "bb", "cc"]}   # {1, 2}

sq = {x: x * x for x in range(1, 4)}    # {1: 1, 2: 4, 3: 9}
unique_lengths = {len(w) for w in ["a", "bb", "cc"]}   # {1, 2}

Q19. append() vs extend() vs insert()?

python

a = [1, 2]
a.append([3, 4])    # [1, 2, [3, 4]]
b = [1, 2]
b.extend([3, 4])    # [1, 2, 3, 4]
c = [1, 3]
c.insert(1, 2)      # [1, 2, 3]

a = [1, 2]
a.append([3, 4])    # [1, 2, [3, 4]]
b = [1, 2]
b.extend([3, 4])    # [1, 2, 3, 4]
c = [1, 3]
c.insert(1, 2)      # [1, 2, 3]

a = [1, 2]
a.append([3, 4])    # [1, 2, [3, 4]]
b = [1, 2]
b.extend([3, 4])    # [1, 2, 3, 4]
c = [1, 3]
c.insert(1, 2)      # [1, 2, 3]

Q20. remove() vs pop() vs del?
remove(x) deletes the first occurrence of a value. pop(i) removes and returns the element at an index (last by default). del a[i] deletes by index or slice.

Q21. Why can a list not be a dictionary key, but a tuple can?
Dictionary keys must be hashable. Lists are mutable and not hashable. A tuple is hashable if all of its elements are hashable.

Q22. How do you remove duplicates from a list?

python

nums = [1, 2, 2, 3, 1]
print(list(set(nums)))                 # order not guaranteed
print(list(dict.fromkeys(nums)))       # [1, 2, 3] keeps first-seen order

nums = [1, 2, 2, 3, 1]
print(list(set(nums)))                 # order not guaranteed
print(list(dict.fromkeys(nums)))       # [1, 2, 3] keeps first-seen order

nums = [1, 2, 2, 3, 1]
print(list(set(nums)))                 # order not guaranteed
print(list(dict.fromkeys(nums)))       # [1, 2, 3] keeps first-seen order

Q23. How do you merge two dictionaries?

python

a = {"x": 1}
b = {"y": 2}
merged = {**a, **b}        # {'x': 1, 'y': 2}
# merged = a | b           # Python 3.9+

a = {"x": 1}
b = {"y": 2}
merged = {**a, **b}        # {'x': 1, 'y': 2}
# merged = a | b           # Python 3.9+

a = {"x": 1}
b = {"y": 2}
merged = {**a, **b}        # {'x': 1, 'y': 2}
# merged = a | b           # Python 3.9+

Q24. How do you safely read a missing key from a dictionary?

python

d = {"a": 1}
print(d.get("b"))          # None
print(d.get("b", 0))       # 0

d = {"a": 1}
print(d.get("b"))          # None
print(d.get("b", 0))       # 0

d = {"a": 1}
print(d.get("b"))          # None
print(d.get("b", 0))       # 0

Q25. What are common string methods?
upper(), lower(), strip(), split(), join(), replace(), find(), startswith(), endswith(), isdigit(), isalpha().

python

words = "a,b,c".split(",")        # ['a', 'b', 'c']
print("-".join(words))            # a-b-c

words = "a,b,c".split(",")        # ['a', 'b', 'c']
print("-".join(words))            # a-b-c

words = "a,b,c".split(",")        # ['a', 'b', 'c']
print("-".join(words))            # a-b-c

Q26. What is the time complexity of membership checks?
On average, x in list is O(n), while x in set and x in dict are O(1). That’s why sets are preferred for fast lookups.

Trap: modifying a list while looping over it, which causes skipped elements.

MODULE 3: FUNCTIONS, SCOPE, LAMBDA, DECORATORS, GENERATORS (Q27 TO Q38)

Q27. How do you define a function with default arguments?

python

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

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

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

Q28. What are *args and **kwargs?

python

def demo(*args, **kwargs):
    print(args)      # tuple of positional arguments
    print(kwargs)    # dict of keyword arguments

demo(1, 2, a=3)      # (1, 2)  {'a': 3}

def demo(*args, **kwargs):
    print(args)      # tuple of positional arguments
    print(kwargs)    # dict of keyword arguments

demo(1, 2, a=3)      # (1, 2)  {'a': 3}

def demo(*args, **kwargs):
    print(args)      # tuple of positional arguments
    print(kwargs)    # dict of keyword arguments

demo(1, 2, a=3)      # (1, 2)  {'a': 3}

Q29. What is the mutable default argument trap?

python

def add(item, bucket=[]):
    bucket.append(item)
    return bucket

print(add(1))   # [1]
print(add(2))   # [1, 2]  <-- surprise

def add(item, bucket=[]):
    bucket.append(item)
    return bucket

print(add(1))   # [1]
print(add(2))   # [1, 2]  <-- surprise

def add(item, bucket=[]):
    bucket.append(item)
    return bucket

print(add(1))   # [1]
print(add(2))   # [1, 2]  <-- surprise

The default list is created once when the function is defined. Fix it:

python

def add(item, bucket=None):
    if bucket is None:
        bucket = []
    bucket.append(item)
    return bucket

def add(item, bucket=None):
    if bucket is None:
        bucket = []
    bucket.append(item)
    return bucket

def add(item, bucket=None):
    if bucket is None:
        bucket = []
    bucket.append(item)
    return bucket

Q30. What is a lambda function?
A small anonymous function: square = lambda x: x * x. Often used with map, filter and sorted.

python

nums = [3, 1, 2]
print(sorted(nums, key=lambda x: -x))              # [3, 2, 1]
print(list(map(lambda x: x * 2, nums)))            # [6, 2, 4]
print(list(filter(lambda x: x > 1, nums)))         # [3, 2]

nums = [3, 1, 2]
print(sorted(nums, key=lambda x: -x))              # [3, 2, 1]
print(list(map(lambda x: x * 2, nums)))            # [6, 2, 4]
print(list(filter(lambda x: x > 1, nums)))         # [3, 2]

nums = [3, 1, 2]
print(sorted(nums, key=lambda x: -x))              # [3, 2, 1]
print(list(map(lambda x: x * 2, nums)))            # [6, 2, 4]
print(list(filter(lambda x: x > 1, nums)))         # [3, 2]

Q31. Explain variable scope (LEGB).
Python looks up names in this order: Local, Enclosing, Global, Built-in.

Q32. What do global and nonlocal do?
global lets a function modify a module-level variable. nonlocal lets a nested function modify a variable in its enclosing function.

Q33. What is recursion? Example?

python

def factorial(n):
    if n <= 1:
        return 1
    return n * factorial(n - 1)

print(factorial(5))   # 120

def factorial(n):
    if n <= 1:
        return 1
    return n * factorial(n - 1)

print(factorial(5))   # 120

def factorial(n):
    if n <= 1:
        return 1
    return n * factorial(n - 1)

print(factorial(5))   # 120

Python limits recursion depth, so very deep recursion raises RecursionError.

Q34. What is a decorator?
A function that wraps another function to add behaviour without changing its code.

python

def log_calls(func):
    def wrapper(*args, **kwargs):
        print(f"Calling {func.__name__}")
        return func(*args, **kwargs)
    return wrapper

@log_calls
def add(a, b):
    return a + b

print(add(2, 3))
# Calling add
# 5

def log_calls(func):
    def wrapper(*args, **kwargs):
        print(f"Calling {func.__name__}")
        return func(*args, **kwargs)
    return wrapper

@log_calls
def add(a, b):
    return a + b

print(add(2, 3))
# Calling add
# 5

def log_calls(func):
    def wrapper(*args, **kwargs):
        print(f"Calling {func.__name__}")
        return func(*args, **kwargs)
    return wrapper

@log_calls
def add(a, b):
    return a + b

print(add(2, 3))
# Calling add
# 5

Q35. What is a generator and how is it different from a list?
A generator produces values lazily, one at a time, using yield. It saves memory because it doesn’t build the whole sequence.

python

def count_up(n):
    for i in range(1, n + 1):
        yield i

for x in count_up(3):
    print(x)          # 1 2 3

gen = (x * x for x in range(1000000))   # generator expression, low memory

def count_up(n):
    for i in range(1, n + 1):
        yield i

for x in count_up(3):
    print(x)          # 1 2 3

gen = (x * x for x in range(1000000))   # generator expression, low memory

def count_up(n):
    for i in range(1, n + 1):
        yield i

for x in count_up(3):
    print(x)          # 1 2 3

gen = (x * x for x in range(1000000))   # generator expression, low memory

Q36. yield vs return?
return ends the function and gives one result. yield pauses the function, returns a value and resumes from the same point on the next request.

Q37. What is an iterator?
An object with __iter__() and __next__() methods. Lists, tuples and strings are iterable, and iter() gives an iterator.

Q38. What are map, filter and reduce?
map applies a function to each item. filter keeps items for which a function returns true. reduce (from functools) combines items into one value.

python

from functools import reduce
print(reduce(lambda a, b: a + b, [1, 2, 3, 4]))   # 10

from functools import reduce
print(reduce(lambda a, b: a + b, [1, 2, 3, 4]))   # 10

from functools import reduce
print(reduce(lambda a, b: a + b, [1, 2, 3, 4]))   # 10

Trap: forgetting that a generator can be consumed only once.

MODULE 4: OOP IN PYTHON (Q39 TO Q47)

Q39. How do you define a class and create an object?

python

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

    def intro(self):
        return f"{self.name}, {self.age}"

s = Student("Amit", 22)
print(s.intro())

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

    def intro(self):
        return f"{self.name}, {self.age}"

s = Student("Amit", 22)
print(s.intro())

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

    def intro(self):
        return f"{self.name}, {self.age}"

s = Student("Amit", 22)
print(s.intro())

Q40. What is self?
A reference to the current instance. By convention it’s the first parameter of instance methods.

Q41. What is __init__?
The initialiser method called when an object is created. It sets up the object’s attributes.

Q42. What are the four OOP pillars in Python?
Encapsulation, inheritance, polymorphism and abstraction.

Q43. How does inheritance work? What is super()?

python

class Animal:
    def speak(self):
        return "Some sound"

class Dog(Animal):
    def speak(self):
        return super().speak() + " -> Woof"

print(Dog().speak())    # Some sound -> Woof

class Animal:
    def speak(self):
        return "Some sound"

class Dog(Animal):
    def speak(self):
        return super().speak() + " -> Woof"

print(Dog().speak())    # Some sound -> Woof

class Animal:
    def speak(self):
        return "Some sound"

class Dog(Animal):
    def speak(self):
        return super().speak() + " -> Woof"

print(Dog().speak())    # Some sound -> Woof

Q44. Does Python support multiple inheritance? What is MRO?
Yes. The Method Resolution Order (MRO) defines the order in which base classes are searched. You can view it with ClassName.__mro__.

Q45. @staticmethod vs @classmethod vs instance method?

Q46. What is encapsulation? Does Python have private variables?
Python has no strict private keyword. By convention, _name means “internal use” and __name triggers name mangling to discourage outside access.

Q47. What are dunder (magic) methods? Examples?
Special methods with double underscores, such as __init__, __str__, __repr__, __len__, __eq__ and __add__.

python

class Point:
    def __init__(self, x, y):
        self.x, self.y = x, y
    def __str__(self):
        return f"Point({self.x}, {self.y})"

print(Point(1, 2))   # Point(1, 2)

class Point:
    def __init__(self, x, y):
        self.x, self.y = x, y
    def __str__(self):
        return f"Point({self.x}, {self.y})"

print(Point(1, 2))   # Point(1, 2)

class Point:
    def __init__(self, x, y):
        self.x, self.y = x, y
    def __str__(self):
        return f"Point({self.x}, {self.y})"

print(Point(1, 2))   # Point(1, 2)

Trap: forgetting self in method definitions.

MODULE 5: EXCEPTIONS, FILES, MODULES AND TOOLS (Q48 TO Q54)

Q48. How does exception handling work

python

try:
    n = int(input("Number: "))
    print(10 / n)
except ValueError:
    print("Enter a valid integer")
except ZeroDivisionError:
    print("Cannot divide by zero")
else:
    print("No error")
finally:
    print("Always runs")

try:
    n = int(input("Number: "))
    print(10 / n)
except ValueError:
    print("Enter a valid integer")
except ZeroDivisionError:
    print("Cannot divide by zero")
else:
    print("No error")
finally:
    print("Always runs")

try:
    n = int(input("Number: "))
    print(10 / n)
except ValueError:
    print("Enter a valid integer")
except ZeroDivisionError:
    print("Cannot divide by zero")
else:
    print("No error")
finally:
    print("Always runs")

Q49. What are else and finally in try?
else runs only if no exception occurred. finally always runs, which makes it useful for cleanup.

Q50. How do you raise your own exception?

python

def set_age(age):
    if age < 0:
        raise ValueError("Age cannot be negative")

def set_age(age):
    if age < 0:
        raise ValueError("Age cannot be negative")

def set_age(age):
    if age < 0:
        raise ValueError("Age cannot be negative")

Q51. How do you read and write files safely?

python

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

with open("data.txt", "r") as f:
    print(f.read())

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

with open("data.txt", "r") as f:
    print(f.read())

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

with open("data.txt", "r") as f:
    print(f.read())

The with statement closes the file automatically, even if an error occurs.

Q52. What is a context manager?
An object that defines setup and cleanup via __enter__ and __exit__, used with the with statement.

Q53. What are modules and packages? What does if __name__ == "__main__" do?
A module is a .py file. A package is a folder of modules. The __name__ check lets code run only when the file is executed directly, not when imported.

Q54. What are pip and virtual environments?
pip installs packages. A virtual environment (python -m venv venv) isolates a project’s dependencies from other projects.

MODULE 6: ADVANCED CONCEPTS FRESHERS ARE ASKED (Q55 TO Q60)

Q55. Shallow copy vs deep copy?

python

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

a[0].append(99)
print(shallow)   # [[1, 2, 99], [3, 4]]  shares inner lists
print(deep)      # [[1, 2], [3, 4]]      independent

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

a[0].append(99)
print(shallow)   # [[1, 2, 99], [3, 4]]  shares inner lists
print(deep)      # [[1, 2], [3, 4]]      independent

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

a[0].append(99)
print(shallow)   # [[1, 2, 99], [3, 4]]  shares inner lists
print(deep)      # [[1, 2], [3, 4]]      independent

Q56. What is the GIL?
In standard CPython, the Global Interpreter Lock lets only one thread execute Python bytecode at a time. Threads help with I/O-bound work, while CPU-bound work often uses multiprocessing. Newer Python releases include experimental builds without the GIL, so check the documentation for your version.

Q57. Threading vs multiprocessing?
Threads share memory within one process and suit I/O-bound tasks. Multiprocessing uses separate processes and can use multiple CPU cores for CPU-bound tasks.

Q58. What is memory management in Python?
Python manages memory automatically using reference counting plus a garbage collector for cyclic references.

Q59. What are type hints?

python

def add(a: int, b: int) -> int:
    return a + b

def add(a: int, b: int) -> int:
    return a + b

def add(a: int, b: int) -> int:
    return a + b

Type hints document expected types and help tools. They aren’t enforced at runtime by default.

Q60. What is match / case?
Structural pattern matching, available from Python 3.10, which lets you match values and shapes.

python

def http_status(code):
    match code:
        case 200:
            return "OK"
        case 404:
            return "Not Found"
        case _:
            return "Other"

def http_status(code):
    match code:
        case 200:
            return "OK"
        case 404:
            return "Not Found"
        case _:
            return "Other"

def http_status(code):
    match code:
        case 200:
            return "OK"
        case 404:
            return "Not Found"
        case _:
            return "Other"

MODULE 7: NUMPY AND PANDAS BASICS (FOR PYTHON + DATA SCIENCE FRESHERS)

Q61. NumPy array vs Python list?
NumPy arrays hold elements of the same type and support fast, vectorised numerical operations. Lists can hold mixed types and are more flexible but typically slower for numerical work.

python

import numpy as np
arr = np.array([1, 2, 3])
print(arr * 2)       # [2 4 6]

import numpy as np
arr = np.array([1, 2, 3])
print(arr * 2)       # [2 4 6]

import numpy as np
arr = np.array([1, 2, 3])
print(arr * 2)       # [2 4 6]

Q62. What is a Pandas Series and DataFrame?
A Series is a one-dimensional labelled array. A DataFrame is a two-dimensional table of rows and columns.

Q63. How do you load and inspect a CSV file?

python

import pandas as pd
df = pd.read_csv("students.csv")
print(df.head())      # first rows
print(df.info())      # column types and null counts
print(df.describe())  # summary statistics

import pandas as pd
df = pd.read_csv("students.csv")
print(df.head())      # first rows
print(df.info())      # column types and null counts
print(df.describe())  # summary statistics

import pandas as pd
df = pd.read_csv("students.csv")
print(df.head())      # first rows
print(df.info())      # column types and null counts
print(df.describe())  # summary statistics

Q64. How do you find and handle missing values?

python

print(df.isnull().sum())          # missing count per column
df = df.dropna()                  # remove rows with missing values
df["marks"] = df["marks"].fillna(df["marks"].mean())   # fill with mean

print(df.isnull().sum())          # missing count per column
df = df.dropna()                  # remove rows with missing values
df["marks"] = df["marks"].fillna(df["marks"].mean())   # fill with mean

print(df.isnull().sum())          # missing count per column
df = df.dropna()                  # remove rows with missing values
df["marks"] = df["marks"].fillna(df["marks"].mean())   # fill with mean

Q65. loc vs iloc?
loc selects by label. iloc selects by integer position.

python

df.loc[0, "name"]     # row label 0, column "name"
df.iloc[0, 1]         # first row, second column

df.loc[0, "name"]     # row label 0, column "name"
df.iloc[0, 1]         # first row, second column

df.loc[0, "name"]     # row label 0, column "name"
df.iloc[0, 1]         # first row, second column

Q66. How do you filter and group data?

python

high = df[df["marks"] > 80]
avg_by_course = df.groupby("course")["marks"].mean()

high = df[df["marks"] > 80]
avg_by_course = df.groupby("course")["marks"].mean()

high = df[df["marks"] > 80]
avg_by_course = df.groupby("course")["marks"].mean()

Q67. How do you merge two DataFrames?

python

result = pd.merge(students, courses, on="course_id", how="left")

result = pd.merge(students, courses, on="course_id", how="left")

result = pd.merge(students, courses, on="course_id", how="left")

Q68. How do you remove duplicates and sort?

python

df = df.drop_duplicates()
df = df.sort_values("marks", ascending=False)

df = df.drop_duplicates()
df = df.sort_values("marks", ascending=False)

df = df.drop_duplicates()
df = df.sort_values("marks", ascending=False)

OUTPUT PREDICTION PUZZLES (COVER THE ANSWER AND TRY FIRST)

Puzzle 1

python

print(0.1 + 0.2 == 0.3)

print(0.1 + 0.2 == 0.3)

print(0.1 + 0.2 == 0.3)

Output: False. Floating-point numbers are stored in binary, so small rounding errors occur. Use math.isclose() for comparisons.

Puzzle 2

python

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

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

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

Output: [1, 2, 3, 4]. b = a copies the reference, not the list.

Puzzle 3

python

def f(x, l=[]):
    l.append(x)
    return l

print(f(1))
print(f(2))

def f(x, l=[]):
    l.append(x)
    return l

print(f(1))
print(f(2))

def f(x, l=[]):
    l.append(x)
    return l

print(f(1))
print(f(2))

Output: [1] then [1, 2]. The mutable default argument is shared across calls.

Puzzle 4

python

print(7 // 2, -7 // 2, 7 % 3, 2 ** 3, 7 / 2)

print(7 // 2, -7 // 2, 7 % 3, 2 ** 3, 7 / 2)

print(7 // 2, -7 // 2, 7 % 3, 2 ** 3, 7 / 2)

Output: 3 -4 1 8 3.5. Floor division rounds toward negative infinity.

Puzzle 5

python

t = (1, 2, [3, 4])
t[2].append(5)
print(t)

t = (1, 2, [3, 4])
t[2].append(5)
print(t)

t = (1, 2, [3, 4])
t[2].append(5)
print(t)

Output: (1, 2, [3, 4, 5]). The tuple is immutable, but the list inside it is mutable.

Puzzle 6

python

print([] == [], [] is [])

print([] == [], [] is [])

print([] == [], [] is [])

Output: True False. Equal values, different objects.

Puzzle 7

python

d = {1: "a", 1.0: "b"}
print(d)

d = {1: "a", 1.0: "b"}
print(d)

d = {1: "a", 1.0: "b"}
print(d)

Output: {1: 'b'}. 1 and 1.0 are equal and have the same hash, so they’re the same key.

Puzzle 8

python

print(bool([]), bool("False"), bool(0), bool(" "))

print(bool([]), bool("False"), bool(0), bool(" "))

print(bool([]), bool("False"), bool(0), bool(" "))

Output: False True False True. Empty collections and zero are falsy, while non-empty strings are truthy (even “False”).

Puzzle 9

python

nums = [1, 2, 3, 4]
for n in nums:
    if n % 2 == 0:
        nums.remove(n)
print(nums)

nums = [1, 2, 3, 4]
for n in nums:
    if n % 2 == 0:
        nums.remove(n)
print(nums)

nums = [1, 2, 3, 4]
for n in nums:
    if n % 2 == 0:
        nums.remove(n)
print(nums)

Output: [1, 3] in this case, but it’s a bad pattern: modifying a list while iterating can skip elements. Use a comprehension to build a new list.

Puzzle 10

python

x = "python"
print(x[1:4], x[-1], x[::2])

x = "python"
print(x[1:4], x[-1], x[::2])

x = "python"
print(x[1:4], x[-1], x[::2])

Output: yth n pto.

15 CODING PROBLEMS WITH SOLUTIONS

1. Check if a string is a palindrome

python

def is_palindrome(s):
    s = s.lower()
    return s == s[::-1]

print(is_palindrome("Madam"))   # True

def is_palindrome(s):
    s = s.lower()
    return s == s[::-1]

print(is_palindrome("Madam"))   # True

def is_palindrome(s):
    s = s.lower()
    return s == s[::-1]

print(is_palindrome("Madam"))   # True

2. Fibonacci series (first n numbers)

python

def fib(n):
    a, b = 0, 1
    result = []
    for _ in range(n):
        result.append(a)
        a, b = b, a + b
    return result

print(fib(8))   # [0, 1, 1, 2, 3, 5, 8, 13]

def fib(n):
    a, b = 0, 1
    result = []
    for _ in range(n):
        result.append(a)
        a, b = b, a + b
    return result

print(fib(8))   # [0, 1, 1, 2, 3, 5, 8, 13]

def fib(n):
    a, b = 0, 1
    result = []
    for _ in range(n):
        result.append(a)
        a, b = b, a + b
    return result

print(fib(8))   # [0, 1, 1, 2, 3, 5, 8, 13]

3. Count vowels in a string

python

def count_vowels(s):
    return sum(1 for ch in s.lower() if ch in "aeiou")

print(count_vowels("Interview"))   # 4

def count_vowels(s):
    return sum(1 for ch in s.lower() if ch in "aeiou")

print(count_vowels("Interview"))   # 4

def count_vowels(s):
    return sum(1 for ch in s.lower() if ch in "aeiou")

print(count_vowels("Interview"))   # 4

4. Find duplicates in a list

python

def find_duplicates(nums):
    seen, dups = set(), set()
    for n in nums:
        if n in seen:
            dups.add(n)
        seen.add(n)
    return list(dups)

print(find_duplicates([1, 2, 3, 2, 4, 1]))   # [1, 2] (order may vary)

def find_duplicates(nums):
    seen, dups = set(), set()
    for n in nums:
        if n in seen:
            dups.add(n)
        seen.add(n)
    return list(dups)

print(find_duplicates([1, 2, 3, 2, 4, 1]))   # [1, 2] (order may vary)

def find_duplicates(nums):
    seen, dups = set(), set()
    for n in nums:
        if n in seen:
            dups.add(n)
        seen.add(n)
    return list(dups)

print(find_duplicates([1, 2, 3, 2, 4, 1]))   # [1, 2] (order may vary)

5. Two-sum (indices of two numbers adding to a target)

python

def two_sum(nums, target):
    seen = {}
    for i, n in enumerate(nums):
        need = target - n
        if need in seen:
            return [seen[need], i]
        seen[n] = i
    return []

print(two_sum([2, 7, 11, 15], 9))   # [0, 1]

def two_sum(nums, target):
    seen = {}
    for i, n in enumerate(nums):
        need = target - n
        if need in seen:
            return [seen[need], i]
        seen[n] = i
    return []

print(two_sum([2, 7, 11, 15], 9))   # [0, 1]

def two_sum(nums, target):
    seen = {}
    for i, n in enumerate(nums):
        need = target - n
        if need in seen:
            return [seen[need], i]
        seen[n] = i
    return []

print(two_sum([2, 7, 11, 15], 9))   # [0, 1]

Complexity: O(n) time using a dictionary.

6. Check for anagrams

python

def is_anagram(a, b):
    return sorted(a.lower()) == sorted(b.lower())

print(is_anagram("listen", "silent"))   # True

def is_anagram(a, b):
    return sorted(a.lower()) == sorted(b.lower())

print(is_anagram("listen", "silent"))   # True

def is_anagram(a, b):
    return sorted(a.lower()) == sorted(b.lower())

print(is_anagram("listen", "silent"))   # True

7. FizzBuzz

python

for i in range(1, 16):
    if i % 15 == 0:
        print("FizzBuzz")
    elif i % 3 == 0:
        print("Fizz")
    elif i % 5 == 0:
        print("Buzz")
    else:
        print(i)

for i in range(1, 16):
    if i % 15 == 0:
        print("FizzBuzz")
    elif i % 3 == 0:
        print("Fizz")
    elif i % 5 == 0:
        print("Buzz")
    else:
        print(i)

for i in range(1, 16):
    if i % 15 == 0:
        print("FizzBuzz")
    elif i % 3 == 0:
        print("Fizz")
    elif i % 5 == 0:
        print("Buzz")
    else:
        print(i)

8. Word frequency count

python

from collections import Counter

text = "to be or not to be"
print(Counter(text.split()))
# Counter({'to': 2, 'be': 2, 'or': 1, 'not': 1})

from collections import Counter

text = "to be or not to be"
print(Counter(text.split()))
# Counter({'to': 2, 'be': 2, 'or': 1, 'not': 1})

from collections import Counter

text = "to be or not to be"
print(Counter(text.split()))
# Counter({'to': 2, 'be': 2, 'or': 1, 'not': 1})

9. Remove duplicates while keeping order

python

nums = [3, 1, 3, 2, 1]
print(list(dict.fromkeys(nums)))   # [3, 1, 2]

nums = [3, 1, 3, 2, 1]
print(list(dict.fromkeys(nums)))   # [3, 1, 2]

nums = [3, 1, 3, 2, 1]
print(list(dict.fromkeys(nums)))   # [3, 1, 2]

10. Find the second largest number

python

def second_largest(nums):
    unique = sorted(set(nums))
    return unique[-2] if len(unique) >= 2 else None

print(second_largest([10, 20, 20, 5]))   # 10

def second_largest(nums):
    unique = sorted(set(nums))
    return unique[-2] if len(unique) >= 2 else None

print(second_largest([10, 20, 20, 5]))   # 10

def second_largest(nums):
    unique = sorted(set(nums))
    return unique[-2] if len(unique) >= 2 else None

print(second_largest([10, 20, 20, 5]))   # 10

11. Check if a number is prime

python

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

print(is_prime(29))   # True

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

print(is_prime(29))   # True

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

print(is_prime(29))   # True

12. Flatten a nested list (one level)

python

nested = [[1, 2], [3], [4, 5]]
flat = [x for sub in nested for x in sub]
print(flat)   # [1, 2, 3, 4, 5]

nested = [[1, 2], [3], [4, 5]]
flat = [x for sub in nested for x in sub]
print(flat)   # [1, 2, 3, 4, 5]

nested = [[1, 2], [3], [4, 5]]
flat = [x for sub in nested for x in sub]
print(flat)   # [1, 2, 3, 4, 5]

13. Count characters using a dictionary

python

s = "hello"
counts = {}
for ch in s:
    counts[ch] = counts.get(ch, 0) + 1
print(counts)   # {'h': 1, 'e': 1, 'l': 2, 'o': 1}

s = "hello"
counts = {}
for ch in s:
    counts[ch] = counts.get(ch, 0) + 1
print(counts)   # {'h': 1, 'e': 1, 'l': 2, 'o': 1}

s = "hello"
counts = {}
for ch in s:
    counts[ch] = counts.get(ch, 0) + 1
print(counts)   # {'h': 1, 'e': 1, 'l': 2, 'o': 1}

14. Swap two variables

python

a, b = 5, 10
a, b = b, a
print(a, b)   # 10 5

a, b = 5, 10
a, b = b, a
print(a, b)   # 10 5

a, b = 5, 10
a, b = b, a
print(a, b)   # 10 5

15. Read a file and count its lines

python

with open("data.txt") as f:
    print(sum(1 for _ in f))

with open("data.txt") as f:
    print(sum(1 for _ in f))

with open("data.txt") as f:
    print(sum(1 for _ in f))

Tip: explain your approach aloud before writing code, mention time complexity, and test with a small example.

SCENARIO AND PROJECT QUESTIONS

COMMON MISTAKES FRESHERS MAKE IN PYTHON INTERVIEWS

MYTH VS REALITY

A 14-DAY PYTHON INTERVIEW PREPARATION PLAN