🐍 Complete Python Interview Preparation Guide

Dost, ye file padh kar tu complete Python interview ready ho jaega! Sab kuch ek jagah hai.

📚 Table of Contents

Core Python Questions
Data Structures & Built-ins
Intermediate Python
Object-Oriented Programming (OOP)
Important Libraries
Problem Solving (DSA)
Common Interview Patterns
Quick Reference

Core Python Questions

Q1: What is Python? What are its key features?

Answer: Python is a high-level, interpreted programming language. Key features:

Simple syntax - Easy to read and write
Interpreted - No compilation needed, runs directly
Dynamic typing - No need to declare variable types
Cross-platform - Works on Windows, Mac, Linux
Large standard library - Many built-in modules
Open source - Free to use and modify

Simple samjho: Python ek aisi language hai jo bilkul English jaisi hai, aur bahut easy hai learn karne ke liye!

Q2: What are the different data types in Python?

Answer:

# Numbers
age = 25          # int (integer)
height = 5.9      # float (decimal)
complex_num = 3+4j # complex
 
# Text
name = "John"     # str (string)
 
# Boolean
is_student = True # bool (True/False)
 
# Collections
fruits = ["apple", "banana"]     # list (mutable)
coordinates = (10, 20)           # tuple (immutable)
person = {"name": "John", "age": 25} # dict (key-value pairs)
unique_nums = {1, 2, 3, 3}      # set (unique elements only)

Simple samjho: Python mein different types ke boxes hote hain different cheezein store karne ke liye!

Q3: What’s the difference between list and tuple?

Answer:

# List - Mutable (changeable)
fruits = ["apple", "banana"]
fruits.append("orange")  # Can add
fruits[0] = "grape"      # Can modify
fruits.remove("banana")  # Can remove
 
# Tuple - Immutable (not changeable)
coordinates = (10, 20)
# coordinates[0] = 15  # ERROR! Cannot modify
# coordinates.append(30)  # ERROR! Cannot add

Key Differences:

List uses [], Tuple uses ()
List is mutable, Tuple is immutable
List has more methods (append, remove, etc.)
Tuple is faster and uses less memory
Tuple can be used as dictionary keys, List cannot

Simple samjho: List jaise shopping list - add/remove kar sakte ho. Tuple jaise birth date - fix hai, change nahi kar sakte!

Q4: What are *args and **kwargs?

Answer:

def my_function(*args, **kwargs):
    print("Positional arguments:", args)
    print("Keyword arguments:", kwargs)
 
# Usage
my_function(1, 2, 3, name="John", age=25)
# Output:
# Positional arguments: (1, 2, 3)
# Keyword arguments: {'name': 'John', 'age': 25}

Explanation:

*args - Accepts any number of positional arguments
**kwargs - Accepts any number of keyword arguments
args becomes a tuple
kwargs becomes a dictionary

Simple samjho: *args matlab “kitne bhi numbers de do”, **kwargs matlab “kitne bhi naam-value pairs de do”!

Q5: What’s the difference between == and is?

Answer:

a = [1, 2, 3]
b = [1, 2, 3]
c = a
 
print(a == b)  # True - same values
print(a is b)  # False - different objects
print(a is c)  # True - same object
 
# Memory addresses
print(id(a))  # 140123456789
print(id(b))  # 140123456790 (different)
print(id(c))  # 140123456789 (same as a)

Key Differences:

== compares values
is compares object identity (memory location)
is is faster than ==
Use is for None, True, False comparisons

Simple samjho: == matlab “value same hai?”, is matlab “same box hai?”

Q6: How do you handle exceptions in Python?

Answer:

try:
    result = 10 / 0  # This will cause an error
    print(result)
except ZeroDivisionError:
    print("Cannot divide by zero!")
except Exception as e:
    print(f"Some other error: {e}")
else:
    print("No errors occurred")
finally:
    print("This always runs")

Exception Types:

try - Code that might cause error
except - Handle specific errors
else - Runs if no error
finally - Always runs
raise - Create your own errors

Simple samjho: Try-except matlab “try karo, agar error aaye to handle kar lo”!

Q7: What are lambda functions?

Answer:

# Regular function
def square(x):
    return x * x
 
# Lambda function (same thing, shorter)
square_lambda = lambda x: x * x
 
# Usage
print(square(5))        # 25
print(square_lambda(5)) # 25
 
# Common use with map, filter
numbers = [1, 2, 3, 4, 5]
squares = list(map(lambda x: x**2, numbers))  # [1, 4, 9, 16, 25]
evens = list(filter(lambda x: x % 2 == 0, numbers))  # [2, 4]

When to use:

Short, simple functions
One-time use functions
With map, filter, reduce
Don’t use for complex logic

Simple samjho: Lambda matlab “chhota function” - ek line mein function bana do!

Data Structures & Built-ins

Q8: How do you remove duplicates from a list?

Answer:

# Method 1: Using set (loses order)
original = [1, 2, 2, 3, 4, 4, 5]
unique = list(set(original))
print(unique)  # [1, 2, 3, 4, 5] (order may vary)
 
# Method 2: Preserving order
unique_ordered = []
for item in original:
    if item not in unique_ordered:
        unique_ordered.append(item)
print(unique_ordered)  # [1, 2, 3, 4, 5] (preserves order)
 
# Method 3: Using dict.fromkeys() (Python 3.7+)
unique_ordered = list(dict.fromkeys(original))
print(unique_ordered)  # [1, 2, 3, 4, 5] (preserves order)

Simple samjho: Set use karo duplicate remove karne ke liye, lekin order maintain karna hai to loop use karo!

Q9: How do you merge two dictionaries?

Answer:

dict1 = {"a": 1, "b": 2}
dict2 = {"c": 3, "d": 4}
 
# Method 1: Using update() (modifies first dict)
dict1.update(dict2)
print(dict1)  # {'a': 1, 'b': 2, 'c': 3, 'd': 4}
 
# Method 2: Using ** unpacking (Python 3.5+)
merged = {**dict1, **dict2}
print(merged)  # {'a': 1, 'b': 2, 'c': 3, 'd': 4}
 
# Method 3: Using | operator (Python 3.9+)
merged = dict1 | dict2
print(merged)  # {'a': 1, 'b': 2, 'c': 3, 'd': 4}

Simple samjho: Dono dictionaries ko combine karna - update() use karo ya ** unpacking use karo!

Q10: What’s the difference between append() and extend()?

Answer:

list1 = [1, 2, 3]
list2 = [1, 2, 3]
 
# append() - adds single element
list1.append([4, 5])
print(list1)  # [1, 2, 3, [4, 5]] - list added as single element
 
# extend() - adds all elements from iterable
list2.extend([4, 5])
print(list2)  # [1, 2, 3, 4, 5] - individual elements added

Key Differences:

append() adds one element to the end
extend() adds all elements from an iterable
append([1,2]) adds the list as one item
extend([1,2]) adds 1 and 2 as separate items

Simple samjho: append() matlab “ek box add karo”, extend() matlab “box ke andar ki cheezein add karo”!

Q11: How do you sort a dictionary by value?

Answer:

scores = {"Alice": 85, "Bob": 92, "Charlie": 78, "Diana": 96}
 
# Method 1: Using sorted() with key parameter
sorted_scores = dict(sorted(scores.items(), key=lambda x: x[1]))
print(sorted_scores)  # {'Charlie': 78, 'Alice': 85, 'Bob': 92, 'Diana': 96}
 
# Method 2: Using operator.itemgetter
from operator import itemgetter
sorted_scores = dict(sorted(scores.items(), key=itemgetter(1)))
print(sorted_scores)  # {'Charlie': 78, 'Alice': 85, 'Bob': 92, 'Diana': 96}
 
# Method 3: Sort in descending order
sorted_scores = dict(sorted(scores.items(), key=lambda x: x[1], reverse=True))
print(sorted_scores)  # {'Diana': 96, 'Bob': 92, 'Alice': 85, 'Charlie': 78}

Simple samjho: Dictionary ko sort karna - sorted() use karo aur key parameter mein lambda function use karo!

Q12: What are list comprehensions?

Answer:

# Regular way
squares = []
for x in range(5):
    squares.append(x**2)
print(squares)  # [0, 1, 4, 9, 16]
 
# List comprehension (shorter way)
squares = [x**2 for x in range(5)]
print(squares)  # [0, 1, 4, 9, 16]
 
# With condition
even_squares = [x**2 for x in range(10) if x % 2 == 0]
print(even_squares)  # [0, 4, 16, 36, 64]
 
# Nested list comprehension
matrix = [[1, 2, 3], [4, 5, 6]]
flattened = [num for row in matrix for num in row]
print(flattened)  # [1, 2, 3, 4, 5, 6]

Simple samjho: List comprehension matlab “list banane ka short way” - ek line mein list bana do!

Intermediate Python

Q13: What are generators in Python?

Answer:

# Generator function
def countdown(n):
    while n > 0:
        yield n  # yield instead of return
        n -= 1
 
# Usage
for i in countdown(5):
    print(i)  # 5, 4, 3, 2, 1
 
# Generator expression
squares = (x**2 for x in range(5))
print(list(squares))  # [0, 1, 4, 9, 16]
 
# Memory efficient
def fibonacci(n):
    a, b = 0, 1
    for _ in range(n):
        yield a
        a, b = b, a + b
 
# Only generates numbers as needed
for num in fibonacci(10):
    print(num)

Key Points:

Use yield instead of return
Memory efficient (lazy evaluation)
Can only iterate once
Good for large datasets

Simple samjho: Generator matlab “on-demand data” - jab chahiye tab generate karo, pehle se store nahi karo!

Q14: What are decorators?

Answer:

# Simple decorator
def my_decorator(func):
    def wrapper():
        print("Something before function")
        func()
        print("Something after function")
    return wrapper
 
@my_decorator
def say_hello():
    print("Hello!")
 
say_hello()
# Output:
# Something before function
# Hello!
# Something after function
 
# Decorator with arguments
def timing_decorator(func):
    import time
    def wrapper(*args, **kwargs):
        start = time.time()
        result = func(*args, **kwargs)
        end = time.time()
        print(f"Function took {end - start} seconds")
        return result
    return wrapper
 
@timing_decorator
def slow_function():
    time.sleep(1)
    return "Done"
 
result = slow_function()  # Function took 1.0 seconds

Simple samjho: Decorator matlab “function ko wrap karna” - extra functionality add karna!

Q15: What’s the difference between shallow copy and deep copy?

Answer:

import copy
 
# Original list with nested list
original = [1, 2, [3, 4]]
 
# Shallow copy
shallow = copy.copy(original)
shallow[2][0] = 999
print(original)   # [1, 2, [999, 4]] - nested list changed!
print(shallow)    # [1, 2, [999, 4]]
 
# Deep copy
original = [1, 2, [3, 4]]
deep = copy.deepcopy(original)
deep[2][0] = 999
print(original)   # [1, 2, [3, 4]] - original unchanged
print(deep)       # [1, 2, [999, 4]]

Key Differences:

Shallow copy - Copies only first level, nested objects are shared
Deep copy - Copies all levels, completely independent
Shallow copy is faster
Deep copy uses more memory

Simple samjho: Shallow copy matlab “surface level copy”, deep copy matlab “complete copy”!

Q16: How do you handle file operations in Python?

Answer:

# Reading a file
with open("data.txt", "r") as file:
    content = file.read()
    print(content)
 
# Writing to a file
with open("output.txt", "w") as file:
    file.write("Hello, World!")
 
# Appending to a file
with open("log.txt", "a") as file:
    file.write("New log entry\n")
 
# Reading line by line (memory efficient)
with open("large_file.txt", "r") as file:
    for line in file:
        print(line.strip())
 
# JSON file operations
import json
 
# Writing JSON
data = {"name": "John", "age": 30}
with open("data.json", "w") as file:
    json.dump(data, file)
 
# Reading JSON
with open("data.json", "r") as file:
    loaded_data = json.load(file)
    print(loaded_data)

Simple samjho: File operations matlab “file se data lena aur file mein data dalna” - always use with statement!

Object-Oriented Programming (OOP)

Q17: What are the four pillars of OOP?

Answer:

# 1. Encapsulation - Data hiding
class BankAccount:
    def __init__(self, balance):
        self.__balance = balance  # Private attribute
    
    def get_balance(self):
        return self.__balance
    
    def deposit(self, amount):
        if amount > 0:
            self.__balance += amount
 
# 2. Inheritance - Reusing code
class Animal:
    def speak(self):
        print("Some sound")
 
class Dog(Animal):
    def speak(self):
        print("Woof!")
 
# 3. Polymorphism - Same interface, different behavior
def make_sound(animal):
    animal.speak()  # Works with any animal
 
dog = Dog()
make_sound(dog)  # Woof!
 
# 4. Abstraction - Hiding complexity
from abc import ABC, abstractmethod
 
class Shape(ABC):
    @abstractmethod
    def area(self):
        pass
 
class Circle(Shape):
    def area(self):
        return 3.14 * self.radius ** 2

Simple samjho: OOP ke 4 pillars - Encapsulation (hide data), Inheritance (reuse code), Polymorphism (same name, different work), Abstraction (hide complexity)!

Q18: What’s the difference between class method and static method?

Answer:

class MyClass:
    class_var = "I'm a class variable"
    
    def __init__(self, value):
        self.instance_var = value
    
    @classmethod
    def class_method(cls):
        print(f"Class method called on {cls}")
        print(f"Class variable: {cls.class_var}")
        return cls("from class method")
    
    @staticmethod
    def static_method():
        print("Static method called")
        return "No access to class or instance"
    
    def instance_method(self):
        print(f"Instance method called on {self}")
        print(f"Instance variable: {self.instance_var}")
 
# Usage
obj = MyClass("test")
 
# Class method - receives class as first argument
new_obj = MyClass.class_method()
 
# Static method - no access to class or instance
result = MyClass.static_method()
 
# Instance method - receives instance as first argument
obj.instance_method()

Key Differences:

Class method - Receives class as first argument, can access class variables
Static method - No access to class or instance, just a function in class namespace
Instance method - Receives instance as first argument, can access instance variables

Simple samjho: Class method matlab “class ke saath kaam”, static method matlab “class mein function”, instance method matlab “object ke saath kaam”!

Q19: What are magic methods (dunder methods)?

Answer:

class Person:
    def __init__(self, name, age):
        self.name = name
        self.age = age
    
    def __str__(self):
        return f"Person: {self.name}, Age: {self.age}"
    
    def __repr__(self):
        return f"Person('{self.name}', {self.age})"
    
    def __len__(self):
        return len(self.name)
    
    def __add__(self, other):
        return Person(f"{self.name} & {other.name}", 
                     (self.age + other.age) // 2)
    
    def __eq__(self, other):
        return self.name == other.name and self.age == other.age
 
# Usage
p1 = Person("Alice", 25)
p2 = Person("Bob", 30)
 
print(p1)           # Person: Alice, Age: 25 (uses __str__)
print(repr(p1))     # Person('Alice', 25) (uses __repr__)
print(len(p1))      # 5 (uses __len__)
print(p1 + p2)      # Person: Alice & Bob, Age: 27 (uses __add__)
print(p1 == p2)     # False (uses __eq__)

Common Magic Methods:

__init__ - Constructor
__str__ - String representation (user-friendly)
__repr__ - String representation (developer-friendly)
__len__ - Length of object
__add__ - Addition operator
__eq__ - Equality comparison

Simple samjho: Magic methods matlab “special functions” - Python automatically call karta hai!

Important Libraries

Q20: How do you work with JSON in Python?

Answer:

import json
 
# Python object to JSON string
data = {
    "name": "John",
    "age": 30,
    "city": "New York",
    "hobbies": ["reading", "coding"]
}
 
# Convert to JSON string
json_string = json.dumps(data, indent=2)
print(json_string)
 
# Write to file
with open("data.json", "w") as file:
    json.dump(data, file, indent=2)
 
# Read from file
with open("data.json", "r") as file:
    loaded_data = json.load(file)
    print(loaded_data)
 
# JSON string to Python object
json_str = '{"name": "Alice", "age": 25}'
python_obj = json.loads(json_str)
print(python_obj)

Simple samjho: JSON matlab “data exchange format” - Python objects ko JSON mein convert karo aur wapas Python mein convert karo!

Q21: How do you handle command line arguments?

Answer:

import sys
import argparse
 
# Method 1: Using sys.argv
print("Command line arguments:", sys.argv)
# python script.py arg1 arg2
# Output: ['script.py', 'arg1', 'arg2']
 
# Method 2: Using argparse (recommended)
parser = argparse.ArgumentParser(description="Process some integers")
parser.add_argument("integers", nargs="+", type=int, help="Integers to sum")
parser.add_argument("--sum", dest="accumulate", action="store_const",
                   const=sum, default=max, help="Sum the integers")
parser.add_argument("--verbose", "-v", action="store_true", help="Verbose output")
 
args = parser.parse_args()
print(args.accumulate(args.integers))

Simple samjho: Command line arguments matlab “script run karte time values dena” - argparse use karo proper handling ke liye!

Q22: How do you work with regular expressions?

Answer:

import re
 
text = "Contact us at support@example.com or call 123-456-7890"
 
# Search for pattern
email_pattern = r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b'
email_match = re.search(email_pattern, text)
if email_match:
    print("Email found:", email_match.group())
 
# Find all matches
phone_pattern = r'\d{3}-\d{3}-\d{4}'
phones = re.findall(phone_pattern, text)
print("Phone numbers:", phones)
 
# Replace text
new_text = re.sub(r'\d{3}-\d{3}-\d{4}', 'XXX-XXX-XXXX', text)
print("Masked text:", new_text)
 
# Split by pattern
words = re.split(r'\s+', text)
print("Words:", words)

Simple samjho: Regular expressions matlab “pattern matching” - text mein specific patterns find karo!

Problem Solving (DSA)

Q23: How do you find the two sum problem?

Answer:

def two_sum(nums, target):
    # Method 1: Brute force O(n²)
    for i in range(len(nums)):
        for j in range(i + 1, len(nums)):
            if nums[i] + nums[j] == target:
                return [i, j]
    return []
 
def two_sum_optimized(nums, target):
    # Method 2: Hash map O(n)
    num_map = {}
    for i, num in enumerate(nums):
        complement = target - num
        if complement in num_map:
            return [num_map[complement], i]
        num_map[num] = i
    return []
 
# Test
nums = [2, 7, 11, 15]
target = 9
print(two_sum(nums, target))        # [0, 1]
print(two_sum_optimized(nums, target))  # [0, 1]

Simple samjho: Two sum matlab “do numbers find karo jo add hoke target bane” - hash map use karo fast solution ke liye!

Q24: How do you reverse a linked list?

Answer:

class ListNode:
    def __init__(self, val=0, next=None):
        self.val = val
        self.next = next
 
def reverse_list(head):
    prev = None
    current = head
    
    while current:
        next_temp = current.next  # Store next node
        current.next = prev       # Reverse the link
        prev = current            # Move prev forward
        current = next_temp       # Move current forward
    
    return prev
 
# Test
# Create list: 1 -> 2 -> 3 -> 4 -> 5
head = ListNode(1)
head.next = ListNode(2)
head.next.next = ListNode(3)
head.next.next.next = ListNode(4)
head.next.next.next.next = ListNode(5)
 
# Reverse it
reversed_head = reverse_list(head)
 
# Print reversed list: 5 -> 4 -> 3 -> 2 -> 1
current = reversed_head
while current:
    print(current.val, end=" -> ")
    current = current.next
print("None")

Simple samjho: Linked list reverse karna matlab “pointers ko ulta karna” - prev, current, next use karo!

Q25: How do you find the maximum subarray sum (Kadane’s Algorithm)?

Answer:

def max_subarray_sum(nums):
    if not nums:
        return 0
    
    max_sum = current_sum = nums[0]
    
    for i in range(1, len(nums)):
        # Either start new subarray or extend existing one
        current_sum = max(nums[i], current_sum + nums[i])
        max_sum = max(max_sum, current_sum)
    
    return max_sum
 
# Test
nums = [-2, 1, -3, 4, -1, 2, 1, -5, 4]
print(max_subarray_sum(nums))  # 6 (subarray [4, -1, 2, 1])

Simple samjho: Maximum subarray sum matlab “continuous numbers ka maximum sum” - Kadane’s algorithm use karo!

Common Interview Patterns

Q26: What are the common algorithmic patterns?

Answer:

# 1. Two Pointers
def two_pointers_example(arr, target):
    left, right = 0, len(arr) - 1
    while left < right:
        current_sum = arr[left] + arr[right]
        if current_sum == target:
            return [left, right]
        elif current_sum < target:
            left += 1
        else:
            right -= 1
    return []
 
# 2. Sliding Window
def sliding_window_example(arr, k):
    window_sum = sum(arr[:k])
    max_sum = window_sum
    
    for i in range(k, len(arr)):
        window_sum = window_sum - arr[i-k] + arr[i]
        max_sum = max(max_sum, window_sum)
    
    return max_sum
 
# 3. Hash Map
def hash_map_example(arr, target):
    num_map = {}
    for i, num in enumerate(arr):
        complement = target - num
        if complement in num_map:
            return [num_map[complement], i]
        num_map[num] = i
    return []
 
# 4. Binary Search
def binary_search(arr, target):
    left, right = 0, len(arr) - 1
    
    while left <= right:
        mid = (left + right) // 2
        if arr[mid] == target:
            return mid
        elif arr[mid] < target:
            left = mid + 1
        else:
            right = mid - 1
    
    return -1

Simple samjho: Common patterns matlab “common solutions” - two pointers, sliding window, hash map, binary search!

Q27: What are the time complexities?

Answer:

# O(1) - Constant time
def get_first_element(arr):
    return arr[0]  # Always takes same time
 
# O(log n) - Logarithmic time
def binary_search(arr, target):
    left, right = 0, len(arr) - 1
    while left <= right:
        mid = (left + right) // 2
        if arr[mid] == target:
            return mid
        elif arr[mid] < target:
            left = mid + 1
        else:
            right = mid - 1
    return -1
 
# O(n) - Linear time
def linear_search(arr, target):
    for i, num in enumerate(arr):
        if num == target:
            return i
    return -1
 
# O(n log n) - Linearithmic time
def merge_sort(arr):
    if len(arr) <= 1:
        return arr
    
    mid = len(arr) // 2
    left = merge_sort(arr[:mid])
    right = merge_sort(arr[mid:])
    
    return merge(left, right)
 
# O(n²) - Quadratic time
def bubble_sort(arr):
    n = len(arr)
    for i in range(n):
        for j in range(0, n - i - 1):
            if arr[j] > arr[j + 1]:
                arr[j], arr[j + 1] = arr[j + 1], arr[j]
    return arr

Simple samjho: Time complexity matlab “kitna time lagega” - O(1) fastest, O(n²) slowest!

Quick Reference

Python Data Types

# Numbers
int_num = 42
float_num = 3.14
complex_num = 3 + 4j
 
# Text
string = "Hello World"
 
# Boolean
boolean = True
 
# Collections
list_data = [1, 2, 3]
tuple_data = (1, 2, 3)
dict_data = {"key": "value"}
set_data = {1, 2, 3}

Common String Methods

text = "  Hello World  "
text.strip()        # "Hello World"
text.upper()        # "  HELLO WORLD  "
text.lower()        # "  hello world  "
text.split()        # ['Hello', 'World']
text.replace("World", "Python")  # "  Hello Python  "

Common List Methods

fruits = ["apple", "banana"]
fruits.append("orange")     # Add to end
fruits.insert(1, "grape")   # Insert at index
fruits.remove("banana")     # Remove first occurrence
fruits.pop()                # Remove last element
fruits.sort()               # Sort in place
fruits.reverse()            # Reverse in place

Common Dictionary Methods

person = {"name": "John", "age": 25}
person["email"] = "john@email.com"  # Add/update
del person["age"]                   # Delete
person.get("name")                  # Get value safely
person.keys()                       # Get all keys
person.values()                     # Get all values
person.items()                      # Get all pairs

File Operations

# Reading
with open("file.txt", "r") as f:
    content = f.read()
 
# Writing
with open("file.txt", "w") as f:
    f.write("Hello World")
 
# Appending
with open("file.txt", "a") as f:
    f.write("New line")

Exception Handling

try:
    # Code that might cause error
    result = 10 / 0
except ZeroDivisionError:
    print("Cannot divide by zero")
except Exception as e:
    print(f"Error: {e}")
finally:
    print("Always runs")

List Comprehensions

# Basic
squares = [x**2 for x in range(5)]
 
# With condition
evens = [x for x in range(10) if x % 2 == 0]
 
# Nested
matrix = [[1, 2, 3], [4, 5, 6]]
flattened = [num for row in matrix for num in row]

Lambda Functions

# Basic
square = lambda x: x**2
 
# With map
numbers = [1, 2, 3, 4, 5]
squares = list(map(lambda x: x**2, numbers))
 
# With filter
evens = list(filter(lambda x: x % 2 == 0, numbers))

OOP Basics

class MyClass:
    def __init__(self, value):
        self.value = value
    
    def method(self):
        return self.value
    
    @classmethod
    def class_method(cls):
        return cls("default")
    
    @staticmethod
    def static_method():
        return "static"

🎯 Interview Tips

Before the Interview

Practice coding - Solve problems daily
Review concepts - Go through this guide
Prepare examples - Have real-world examples ready
Mock interviews - Practice with friends

During the Interview

Think out loud - Explain your thought process
Ask questions - Clarify requirements
Start simple - Begin with brute force, then optimize
Test your code - Walk through examples
Handle edge cases - Consider empty inputs, single elements

Common Mistakes to Avoid

Don’t jump to code - Plan first
Don’t ignore edge cases - Handle them
Don’t use complex solutions - Keep it simple
Don’t forget to test - Verify your solution
Don’t give up - Keep trying different approaches

🚀 Final Words

Dost, ye complete guide hai Python interview ke liye!

Key Points to Remember:

Practice regularly - Coding is a skill that improves with practice
Understand concepts - Don’t just memorize, understand the why
Build projects - Apply what you learn in real projects
Stay updated - Python evolves, keep learning new features
Be confident - You’ve got this!

Good luck with your interviews! 🍀

Made with ❤️ for Python learners

Notes

Explorer

Python_interview

🐍 Complete Python Interview Preparation Guide

📚 Table of Contents

Core Python Questions

Q1: What is Python? What are its key features?

Q2: What are the different data types in Python?

Q3: What’s the difference between list and tuple?

Q4: What are *args and **kwargs?

Q5: What’s the difference between == and is?

Q6: How do you handle exceptions in Python?

Q7: What are lambda functions?

Data Structures & Built-ins

Q8: How do you remove duplicates from a list?

Q9: How do you merge two dictionaries?

Q10: What’s the difference between append() and extend()?

Q11: How do you sort a dictionary by value?

Q12: What are list comprehensions?

Intermediate Python

Q13: What are generators in Python?

Q14: What are decorators?

Q15: What’s the difference between shallow copy and deep copy?

Q16: How do you handle file operations in Python?

Object-Oriented Programming (OOP)

Q17: What are the four pillars of OOP?

Q18: What’s the difference between class method and static method?

Q19: What are magic methods (dunder methods)?

Important Libraries

Q20: How do you work with JSON in Python?

Q21: How do you handle command line arguments?

Q22: How do you work with regular expressions?

Problem Solving (DSA)

Q23: How do you find the two sum problem?

Q24: How do you reverse a linked list?

Q25: How do you find the maximum subarray sum (Kadane’s Algorithm)?

Common Interview Patterns

Q26: What are the common algorithmic patterns?

Q27: What are the time complexities?

Quick Reference

Python Data Types

Common String Methods

Common List Methods

Common Dictionary Methods

File Operations

Exception Handling

List Comprehensions

Lambda Functions

OOP Basics

🎯 Interview Tips

Before the Interview

During the Interview

Common Mistakes to Avoid

🚀 Final Words

Graph View

Table of Contents