Python Interview Questions:
Ready to test your Python skills? Letโs get started! ๐ป
1. How to check if a string is a palindrome?
2. How to find the factorial of a number using recursion?
3. How to merge two dictionaries in Python?
4. How to find the intersection of two lists?
5. How to generate a list of even numbers from 1 to 100?
6. How to find the longest word in a sentence?
7. How to count the frequency of elements in a list?
8. How to remove duplicates from a list while maintaining the order?
9. How to reverse a linked list in Python?
10. How to implement a simple binary search algorithm?
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https://t.iss.one/DataSimplifier
Like for more resources like this ๐ โฅ๏ธ
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Hope it helps :)
Ready to test your Python skills? Letโs get started! ๐ป
1. How to check if a string is a palindrome?
def is_palindrome(s):
return s == s[::-1]
print(is_palindrome("madam")) # True
print(is_palindrome("hello")) # False
2. How to find the factorial of a number using recursion?
def factorial(n):
if n == 0 or n == 1:
return 1
return n * factorial(n - 1)
print(factorial(5)) # 120
3. How to merge two dictionaries in Python?
dict1 = {'a': 1, 'b': 2}
dict2 = {'c': 3, 'd': 4}
# Method 1 (Python 3.5+)
merged_dict = {**dict1, **dict2}
# Method 2 (Python 3.9+)
merged_dict = dict1 | dict2
print(merged_dict)
4. How to find the intersection of two lists?
list1 = [1, 2, 3, 4]
list2 = [3, 4, 5, 6]
intersection = list(set(list1) & set(list2))
print(intersection) # [3, 4]
5. How to generate a list of even numbers from 1 to 100?
even_numbers = [i for i in range(1, 101) if i % 2 == 0]
print(even_numbers)
6. How to find the longest word in a sentence?
def longest_word(sentence):
words = sentence.split()
return max(words, key=len)
print(longest_word("Python is a powerful language")) # "powerful"
7. How to count the frequency of elements in a list?
from collections import Counter
my_list = [1, 2, 2, 3, 3, 3, 4]
frequency = Counter(my_list)
print(frequency) # Counter({3: 3, 2: 2, 1: 1, 4: 1})
8. How to remove duplicates from a list while maintaining the order?
def remove_duplicates(lst):
return list(dict.fromkeys(lst))
my_list = [1, 2, 2, 3, 4, 4, 5]
print(remove_duplicates(my_list)) # [1, 2, 3, 4, 5]
9. How to reverse a linked list in Python?
class Node:
def __init__(self, data):
self.data = data
self.next = None
def reverse_linked_list(head):
prev = None
current = head
while current:
next_node = current.next
current.next = prev
prev = current
current = next_node
return prev
# Create linked list: 1 -> 2 -> 3
head = Node(1)
head.next = Node(2)
head.next.next = Node(3)
# Reverse and print the list
reversed_head = reverse_linked_list(head)
while reversed_head:
print(reversed_head.data, end=" -> ")
reversed_head = reversed_head.next
10. How to implement a simple binary search algorithm?
def binary_search(arr, target):
low, high = 0, len(arr) - 1
while low <= high:
mid = (low + high) // 2
if arr[mid] == target:
return mid
elif arr[mid] < target:
low = mid + 1
else:
high = mid - 1
return -1
print(binary_search([1, 2, 3, 4, 5, 6, 7], 4)) # 3
Here you can find essential Python Interview Resources๐
https://t.iss.one/DataSimplifier
Like for more resources like this ๐ โฅ๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โค5๐5๐ฅฐ5
Python Interview Questions:
Ready to test your Python skills? Letโs get started! ๐ป
1. How to check if a string is a palindrome?
2. How to find the factorial of a number using recursion?
3. How to merge two dictionaries in Python?
4. How to find the intersection of two lists?
5. How to generate a list of even numbers from 1 to 100?
6. How to find the longest word in a sentence?
7. How to count the frequency of elements in a list?
8. How to remove duplicates from a list while maintaining the order?
9. How to reverse a linked list in Python?
10. How to implement a simple binary search algorithm?
Here you can find essential Python Interview Resources๐
https://t.iss.one/DataSimplifier
Like for more resources like this ๐ โฅ๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Ready to test your Python skills? Letโs get started! ๐ป
1. How to check if a string is a palindrome?
def is_palindrome(s):
return s == s[::-1]
print(is_palindrome("madam")) # True
print(is_palindrome("hello")) # False
2. How to find the factorial of a number using recursion?
def factorial(n):
if n == 0 or n == 1:
return 1
return n * factorial(n - 1)
print(factorial(5)) # 120
3. How to merge two dictionaries in Python?
dict1 = {'a': 1, 'b': 2}
dict2 = {'c': 3, 'd': 4}
# Method 1 (Python 3.5+)
merged_dict = {**dict1, **dict2}
# Method 2 (Python 3.9+)
merged_dict = dict1 | dict2
print(merged_dict)
4. How to find the intersection of two lists?
list1 = [1, 2, 3, 4]
list2 = [3, 4, 5, 6]
intersection = list(set(list1) & set(list2))
print(intersection) # [3, 4]
5. How to generate a list of even numbers from 1 to 100?
even_numbers = [i for i in range(1, 101) if i % 2 == 0]
print(even_numbers)
6. How to find the longest word in a sentence?
def longest_word(sentence):
words = sentence.split()
return max(words, key=len)
print(longest_word("Python is a powerful language")) # "powerful"
7. How to count the frequency of elements in a list?
from collections import Counter
my_list = [1, 2, 2, 3, 3, 3, 4]
frequency = Counter(my_list)
print(frequency) # Counter({3: 3, 2: 2, 1: 1, 4: 1})
8. How to remove duplicates from a list while maintaining the order?
def remove_duplicates(lst):
return list(dict.fromkeys(lst))
my_list = [1, 2, 2, 3, 4, 4, 5]
print(remove_duplicates(my_list)) # [1, 2, 3, 4, 5]
9. How to reverse a linked list in Python?
class Node:
def __init__(self, data):
self.data = data
self.next = None
def reverse_linked_list(head):
prev = None
current = head
while current:
next_node = current.next
current.next = prev
prev = current
current = next_node
return prev
# Create linked list: 1 -> 2 -> 3
head = Node(1)
head.next = Node(2)
head.next.next = Node(3)
# Reverse and print the list
reversed_head = reverse_linked_list(head)
while reversed_head:
print(reversed_head.data, end=" -> ")
reversed_head = reversed_head.next
10. How to implement a simple binary search algorithm?
def binary_search(arr, target):
low, high = 0, len(arr) - 1
while low <= high:
mid = (low + high) // 2
if arr[mid] == target:
return mid
elif arr[mid] < target:
low = mid + 1
else:
high = mid - 1
return -1
print(binary_search([1, 2, 3, 4, 5, 6, 7], 4)) # 3
Here you can find essential Python Interview Resources๐
https://t.iss.one/DataSimplifier
Like for more resources like this ๐ โฅ๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
๐6โค5
If you're a software engineer in your 20s, beware of this habit, it can kill your growth faster than anything else.
โบ Fake learning.
It feels productive, but it's not.
Let me give you a great example:
You wake up fired up.
Open YouTube, start a system design video.
An hour goes by. You nod, you get it (or so you think).
You switch to a course on Spring Boot. Build a to-do app.
Then read a blog on Kafka. Scroll through a thread on Redis.
By evening, you feel like youโve had a productive day.
But two weeks later?
You canโt recall a single implementation detail.
You havenโt written a line of code around those topics.
You just consumed, but never applied.
Thatโs fake learning.
Itโs learning without doing.
It gives you the illusion of growth, while keeping you stuck.
๐ Hereโs how to fix it:
Watch fewer tutorials. Build more things.
Learn with a goal: โIโll use this to build X.โ
After every video, write your own summary.
Recode it from scratch.
Start documenting what you really understood vs. what felt easy.
Real growth happens when you struggle.
When you break things. When you debug.
Passive learning is comfortable.
But discomfort is where the actual skills are built.
Your 20s are for laying that solid technical foundation.
Donโt waste them just โwatching smart.โ
Build. Ship. Reflect.
Thatโs how you grow.
Coding Projects:๐
https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502
ENJOY LEARNING ๐๐
โบ Fake learning.
It feels productive, but it's not.
Let me give you a great example:
You wake up fired up.
Open YouTube, start a system design video.
An hour goes by. You nod, you get it (or so you think).
You switch to a course on Spring Boot. Build a to-do app.
Then read a blog on Kafka. Scroll through a thread on Redis.
By evening, you feel like youโve had a productive day.
But two weeks later?
You canโt recall a single implementation detail.
You havenโt written a line of code around those topics.
You just consumed, but never applied.
Thatโs fake learning.
Itโs learning without doing.
It gives you the illusion of growth, while keeping you stuck.
๐ Hereโs how to fix it:
Watch fewer tutorials. Build more things.
Learn with a goal: โIโll use this to build X.โ
After every video, write your own summary.
Recode it from scratch.
Start documenting what you really understood vs. what felt easy.
Real growth happens when you struggle.
When you break things. When you debug.
Passive learning is comfortable.
But discomfort is where the actual skills are built.
Your 20s are for laying that solid technical foundation.
Donโt waste them just โwatching smart.โ
Build. Ship. Reflect.
Thatโs how you grow.
Coding Projects:๐
https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502
ENJOY LEARNING ๐๐
โค4๐4
Artificial Intelligence (AI) is the simulation of human intelligence in machines that are designed to think, learn, and make decisions. From virtual assistants to self-driving cars, AI is transforming how we interact with technology.
Hers is the brief A-Z overview of the terms used in Artificial Intelligence World
A - Algorithm: A set of rules or instructions that an AI system follows to solve problems or make decisions.
B - Bias: Prejudice in AI systems due to skewed training data, leading to unfair outcomes.
C - Chatbot: AI software that can hold conversations with users via text or voice.
D - Deep Learning: A type of machine learning using layered neural networks to analyze data and make decisions.
E - Expert System: An AI that replicates the decision-making ability of a human expert in a specific domain.
F - Fine-Tuning: The process of refining a pre-trained model on a specific task or dataset.
G - Generative AI: AI that can create new content like text, images, audio, or code.
H - Heuristic: A rule-of-thumb or shortcut used by AI to make decisions efficiently.
I - Image Recognition: The ability of AI to detect and classify objects or features in an image.
J - Jupyter Notebook: A tool widely used in AI for interactive coding, data visualization, and documentation.
K - Knowledge Representation: How AI systems store, organize, and use information for reasoning.
L - LLM (Large Language Model): An AI trained on large text datasets to understand and generate human language (e.g., GPT-4).
M - Machine Learning: A branch of AI where systems learn from data instead of being explicitly programmed.
N - NLP (Natural Language Processing): AI's ability to understand, interpret, and generate human language.
O - Overfitting: When a model performs well on training data but poorly on unseen data due to memorizing instead of generalizing.
P - Prompt Engineering: Crafting effective inputs to steer generative AI toward desired responses.
Q - Q-Learning: A reinforcement learning algorithm that helps agents learn the best actions to take.
R - Reinforcement Learning: A type of learning where AI agents learn by interacting with environments and receiving rewards.
S - Supervised Learning: Machine learning where models are trained on labeled datasets.
T - Transformer: A neural network architecture powering models like GPT and BERT, crucial in NLP tasks.
U - Unsupervised Learning: A method where AI finds patterns in data without labeled outcomes.
V - Vision (Computer Vision): The field of AI that enables machines to interpret and process visual data.
W - Weak AI: AI designed to handle narrow tasks without consciousness or general intelligence.
X - Explainable AI (XAI): Techniques that make AI decision-making transparent and understandable to humans.
Y - YOLO (You Only Look Once): A popular real-time object detection algorithm in computer vision.
Z - Zero-shot Learning: The ability of AI to perform tasks it hasnโt been explicitly trained on.
Credits: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
Hers is the brief A-Z overview of the terms used in Artificial Intelligence World
A - Algorithm: A set of rules or instructions that an AI system follows to solve problems or make decisions.
B - Bias: Prejudice in AI systems due to skewed training data, leading to unfair outcomes.
C - Chatbot: AI software that can hold conversations with users via text or voice.
D - Deep Learning: A type of machine learning using layered neural networks to analyze data and make decisions.
E - Expert System: An AI that replicates the decision-making ability of a human expert in a specific domain.
F - Fine-Tuning: The process of refining a pre-trained model on a specific task or dataset.
G - Generative AI: AI that can create new content like text, images, audio, or code.
H - Heuristic: A rule-of-thumb or shortcut used by AI to make decisions efficiently.
I - Image Recognition: The ability of AI to detect and classify objects or features in an image.
J - Jupyter Notebook: A tool widely used in AI for interactive coding, data visualization, and documentation.
K - Knowledge Representation: How AI systems store, organize, and use information for reasoning.
L - LLM (Large Language Model): An AI trained on large text datasets to understand and generate human language (e.g., GPT-4).
M - Machine Learning: A branch of AI where systems learn from data instead of being explicitly programmed.
N - NLP (Natural Language Processing): AI's ability to understand, interpret, and generate human language.
O - Overfitting: When a model performs well on training data but poorly on unseen data due to memorizing instead of generalizing.
P - Prompt Engineering: Crafting effective inputs to steer generative AI toward desired responses.
Q - Q-Learning: A reinforcement learning algorithm that helps agents learn the best actions to take.
R - Reinforcement Learning: A type of learning where AI agents learn by interacting with environments and receiving rewards.
S - Supervised Learning: Machine learning where models are trained on labeled datasets.
T - Transformer: A neural network architecture powering models like GPT and BERT, crucial in NLP tasks.
U - Unsupervised Learning: A method where AI finds patterns in data without labeled outcomes.
V - Vision (Computer Vision): The field of AI that enables machines to interpret and process visual data.
W - Weak AI: AI designed to handle narrow tasks without consciousness or general intelligence.
X - Explainable AI (XAI): Techniques that make AI decision-making transparent and understandable to humans.
Y - YOLO (You Only Look Once): A popular real-time object detection algorithm in computer vision.
Z - Zero-shot Learning: The ability of AI to perform tasks it hasnโt been explicitly trained on.
Credits: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
๐7โค1
๐ฆ๐๐ฒ๐ฝ๐ ๐ง๐ผ ๐ฃ๐ฟ๐ฒ๐ฝ๐ฎ๐ฟ๐ฒ ๐๐ผ๐ฟ ๐ฎ ๐ง๐ฒ๐ฐ๐ต๐ป๐ถ๐ฐ๐ฎ๐น ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐
๐ ๐๐ป๐ผ๐ ๐๐ต๐ฒ ๐๐ผ๐ฏ: Review the job description.
๐ ๐๐ฎ๐๐ถ๐ฐ๐: Revise fundamental concepts.
๐ ๐๐ผ๐ฑ๐ฒ ๐ฃ๐ฟ๐ฎ๐ฐ๐๐ถ๐ฐ๐ฒ: Solve coding problems.
๐ ๐ฃ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐๐: Be ready to discuss past work.
๐ ๐ ๐ผ๐ฐ๐ธ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐๐: Practice with friends or online.
๐ ๐ฆ๐๐๐๐ฒ๐บ ๐๐ฒ๐๐ถ๐ด๐ป: Review basics if needed.
๐ ๐ค๐๐ฒ๐๐๐ถ๐ผ๐ป๐: Prepare some for the interviewer.
๐ ๐ฅ๐ฒ๐๐: Sleep well and stay calm.
Remember, practice and confidence are the key! Good luck with your technical interview! ๐๐
You can check these resources for Coding interview Preparation
All the best ๐๐
๐ ๐๐ป๐ผ๐ ๐๐ต๐ฒ ๐๐ผ๐ฏ: Review the job description.
๐ ๐๐ฎ๐๐ถ๐ฐ๐: Revise fundamental concepts.
๐ ๐๐ผ๐ฑ๐ฒ ๐ฃ๐ฟ๐ฎ๐ฐ๐๐ถ๐ฐ๐ฒ: Solve coding problems.
๐ ๐ฃ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐๐: Be ready to discuss past work.
๐ ๐ ๐ผ๐ฐ๐ธ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐๐: Practice with friends or online.
๐ ๐ฆ๐๐๐๐ฒ๐บ ๐๐ฒ๐๐ถ๐ด๐ป: Review basics if needed.
๐ ๐ค๐๐ฒ๐๐๐ถ๐ผ๐ป๐: Prepare some for the interviewer.
๐ ๐ฅ๐ฒ๐๐: Sleep well and stay calm.
Remember, practice and confidence are the key! Good luck with your technical interview! ๐๐
You can check these resources for Coding interview Preparation
All the best ๐๐
๐5โค1
Python Full Stack Developer Roadmap:
Stage 1: HTML โ Learn webpage basics.
Stage 2: CSS โ Style web pages.
Stage 3: JavaScript โ Add interactivity.
Stage 4: Git + GitHub โ Manage code versions.
Stage 5: Frontend Project โ Build a simple project.
Stage 6: Python (Core + OOP) โ Learn Python fundamentals.
Stage 7: Backend Project โ Use Flask/Django for backend.
Stage 8: Frameworks โ Master Flask/Django features.
Stage 1: HTML โ Learn webpage basics.
Stage 2: CSS โ Style web pages.
Stage 3: JavaScript โ Add interactivity.
Stage 4: Git + GitHub โ Manage code versions.
Stage 5: Frontend Project โ Build a simple project.
Stage 6: Python (Core + OOP) โ Learn Python fundamentals.
Stage 7: Backend Project โ Use Flask/Django for backend.
Stage 8: Frameworks โ Master Flask/Django features.
๐6โค1๐ฅ1
Here are some of the top Python frameworks for web development:
1. Django: A high-level framework that encourages rapid development and clean, pragmatic design. It includes a built-in admin interface, ORM, and many other features.
2. Flask: A micro-framework that is lightweight and easy to set up, making it a popular choice for small to medium-sized projects. It provides the essentials and leaves the rest to extensions.
3. FastAPI: Known for its high performance and ease of use, FastAPI is ideal for building APIs. It supports asynchronous programming and is built on standard Python type hints.
4. Pyramid: A flexible framework that can be used for both small applications and large-scale projects. It provides a minimalistic core with optional add-ons for added functionality.
5. Tornado: Designed for handling large numbers of simultaneous connections, making it a good choice for applications that require real-time capabilities.
6. Bottle: A very lightweight micro-framework that is perfect for small web applications. It is contained in a single file and has no dependencies other than the Python Standard Library.
7. CherryPy: An object-oriented framework that allows developers to build web applications in a similar way to writing other Python programs. It is minimalistic and easy to use.
8. Web2py: A full-stack framework that includes an integrated development environment, a web-based interface, and a web server. It emphasizes ease of use and rapid development.
9. Sanic: An asynchronous framework built for speed. It is designed to handle large volumes of traffic and is well-suited for building fast APIs.
10. Falcon: Another framework focused on building fast APIs. Falcon is lightweight and focuses on performance and reliability.
Free Resources to learn web development https://t.iss.one/free4unow_backup/554
Web Development Best Resources: https://topmate.io/coding/930165
ENJOY LEARNING ๐๐
1. Django: A high-level framework that encourages rapid development and clean, pragmatic design. It includes a built-in admin interface, ORM, and many other features.
2. Flask: A micro-framework that is lightweight and easy to set up, making it a popular choice for small to medium-sized projects. It provides the essentials and leaves the rest to extensions.
3. FastAPI: Known for its high performance and ease of use, FastAPI is ideal for building APIs. It supports asynchronous programming and is built on standard Python type hints.
4. Pyramid: A flexible framework that can be used for both small applications and large-scale projects. It provides a minimalistic core with optional add-ons for added functionality.
5. Tornado: Designed for handling large numbers of simultaneous connections, making it a good choice for applications that require real-time capabilities.
6. Bottle: A very lightweight micro-framework that is perfect for small web applications. It is contained in a single file and has no dependencies other than the Python Standard Library.
7. CherryPy: An object-oriented framework that allows developers to build web applications in a similar way to writing other Python programs. It is minimalistic and easy to use.
8. Web2py: A full-stack framework that includes an integrated development environment, a web-based interface, and a web server. It emphasizes ease of use and rapid development.
9. Sanic: An asynchronous framework built for speed. It is designed to handle large volumes of traffic and is well-suited for building fast APIs.
10. Falcon: Another framework focused on building fast APIs. Falcon is lightweight and focuses on performance and reliability.
Free Resources to learn web development https://t.iss.one/free4unow_backup/554
Web Development Best Resources: https://topmate.io/coding/930165
ENJOY LEARNING ๐๐
๐2โค1
Python Roadmap for 2025: Complete Guide
1. Python Fundamentals
1.1 Variables, constants, and comments.
1.2 Data types: int, float, str, bool, complex.
1.3 Input and output (input(), print(), formatted strings).
1.4 Python syntax: Indentation and code structure.
2. Operators
2.1 Arithmetic: +, -, *, /, %, //, **.
2.2 Comparison: ==, !=, <, >, <=, >=.
2.3 Logical: and, or, not.
2.4 Bitwise: &, |, ^, ~, <<, >>.
2.5 Identity: is, is not.
2.6 Membership: in, not in.
3. Control Flow
3.1 Conditional statements: if, elif, else.
3.2 Loops: for, while.
3.3 Loop control: break, continue, pass.
4. Data Structures
4.1 Lists: Indexing, slicing, methods (append(), pop(), sort(), etc.).
4.2 Tuples: Immutability, packing/unpacking.
4.3 Dictionaries: Key-value pairs, methods (get(), items(), etc.).
4.4 Sets: Unique elements, set operations (union, intersection).
4.5 Strings: Immutability, methods (split(), strip(), replace()).
5. Functions
5.1 Defining functions with def.
5.2 Arguments: Positional, keyword, default, *args, **kwargs.
5.3 Anonymous functions (lambda).
5.4 Recursion.
6. Modules and Packages
6.1 Importing: import, from ... import.
6.2 Standard libraries: math, os, sys, random, datetime, time.
6.3 Installing external libraries with pip.
7. File Handling
7.1 Open and close files (open(), close()).
7.2 Read and write (read(), write(), readlines()).
7.3 Using context managers (with open(...)).
8. Object-Oriented Programming (OOP)
8.1 Classes and objects.
8.2 Methods and attributes.
8.3 Constructor (init).
8.4 Inheritance, polymorphism, encapsulation.
8.5 Special methods (str, repr, etc.).
9. Error and Exception Handling
9.1 try, except, else, finally.
9.2 Raising exceptions (raise).
9.3 Custom exceptions.
10. Comprehensions
10.1 List comprehensions.
10.2 Dictionary comprehensions.
10.3 Set comprehensions.
11. Iterators and Generators
11.1 Creating iterators using iter() and next().
11.2 Generators with yield.
11.3 Generator expressions.
12. Decorators and Closures
12.1 Functions as first-class citizens.
12.2 Nested functions.
12.3 Closures.
12.4 Creating and applying decorators.
13. Advanced Topics
13.1 Context managers (with statement).
13.2 Multithreading and multiprocessing.
13.3 Asynchronous programming with async and await.
13.4 Python's Global Interpreter Lock (GIL).
14. Python Internals
14.1 Mutable vs immutable objects.
14.2 Memory management and garbage collection.
14.3 Python's name == "main" mechanism.
15. Libraries and Frameworks
15.1 Data Science: NumPy, Pandas, Matplotlib, Seaborn.
15.2 Web Development: Flask, Django, FastAPI.
15.3 Testing: unittest, pytest.
15.4 APIs: requests, http.client.
15.5 Automation: selenium, os.
15.6 Machine Learning: scikit-learn, TensorFlow, PyTorch.
16. Tools and Best Practices
16.1 Debugging: pdb, breakpoints.
16.2 Code style: PEP 8 guidelines.
16.3 Virtual environments: venv.
16.4 Version control: Git + GitHub.
๐ Python Interview ๐ฅ๐ฒ๐๐ผ๐๐ฟ๐ฐ๐ฒ๐
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๐ ๐ฃ๐ฟ๐ฒ๐บ๐ถ๐๐บ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐ ๐ฅ๐ฒ๐๐ผ๐๐ฟ๐ฐ๐ฒ๐ : https://topmate.io/coding/914624
๐ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ: https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z
Join What's app channel for jobs updates: t.iss.one/getjobss
1. Python Fundamentals
1.1 Variables, constants, and comments.
1.2 Data types: int, float, str, bool, complex.
1.3 Input and output (input(), print(), formatted strings).
1.4 Python syntax: Indentation and code structure.
2. Operators
2.1 Arithmetic: +, -, *, /, %, //, **.
2.2 Comparison: ==, !=, <, >, <=, >=.
2.3 Logical: and, or, not.
2.4 Bitwise: &, |, ^, ~, <<, >>.
2.5 Identity: is, is not.
2.6 Membership: in, not in.
3. Control Flow
3.1 Conditional statements: if, elif, else.
3.2 Loops: for, while.
3.3 Loop control: break, continue, pass.
4. Data Structures
4.1 Lists: Indexing, slicing, methods (append(), pop(), sort(), etc.).
4.2 Tuples: Immutability, packing/unpacking.
4.3 Dictionaries: Key-value pairs, methods (get(), items(), etc.).
4.4 Sets: Unique elements, set operations (union, intersection).
4.5 Strings: Immutability, methods (split(), strip(), replace()).
5. Functions
5.1 Defining functions with def.
5.2 Arguments: Positional, keyword, default, *args, **kwargs.
5.3 Anonymous functions (lambda).
5.4 Recursion.
6. Modules and Packages
6.1 Importing: import, from ... import.
6.2 Standard libraries: math, os, sys, random, datetime, time.
6.3 Installing external libraries with pip.
7. File Handling
7.1 Open and close files (open(), close()).
7.2 Read and write (read(), write(), readlines()).
7.3 Using context managers (with open(...)).
8. Object-Oriented Programming (OOP)
8.1 Classes and objects.
8.2 Methods and attributes.
8.3 Constructor (init).
8.4 Inheritance, polymorphism, encapsulation.
8.5 Special methods (str, repr, etc.).
9. Error and Exception Handling
9.1 try, except, else, finally.
9.2 Raising exceptions (raise).
9.3 Custom exceptions.
10. Comprehensions
10.1 List comprehensions.
10.2 Dictionary comprehensions.
10.3 Set comprehensions.
11. Iterators and Generators
11.1 Creating iterators using iter() and next().
11.2 Generators with yield.
11.3 Generator expressions.
12. Decorators and Closures
12.1 Functions as first-class citizens.
12.2 Nested functions.
12.3 Closures.
12.4 Creating and applying decorators.
13. Advanced Topics
13.1 Context managers (with statement).
13.2 Multithreading and multiprocessing.
13.3 Asynchronous programming with async and await.
13.4 Python's Global Interpreter Lock (GIL).
14. Python Internals
14.1 Mutable vs immutable objects.
14.2 Memory management and garbage collection.
14.3 Python's name == "main" mechanism.
15. Libraries and Frameworks
15.1 Data Science: NumPy, Pandas, Matplotlib, Seaborn.
15.2 Web Development: Flask, Django, FastAPI.
15.3 Testing: unittest, pytest.
15.4 APIs: requests, http.client.
15.5 Automation: selenium, os.
15.6 Machine Learning: scikit-learn, TensorFlow, PyTorch.
16. Tools and Best Practices
16.1 Debugging: pdb, breakpoints.
16.2 Code style: PEP 8 guidelines.
16.3 Virtual environments: venv.
16.4 Version control: Git + GitHub.
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