Python Programming & AI Resources
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✅ Python Programming Books ✅ Coding Projects ✅ Important Pdfs ✅ Artificial Intelligence Courses ✅ Data Science Notes For promotions: @love_data Buy ads: https://telega.io/c/pythonproz
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Python Programming & AI Resources (@pythonproz) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 13 506 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 9 206-o'rinni va Hindiston mintaqasida 30 035-o'rinni egallagan.
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25 Avgust, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 124 ga, so‘nggi 24 soatda esa -6 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
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- Tematik yo‘nalishlar: Kontent tuple, comprehension, learning, programming, loop kabi asosiy mavzularga jamlangan.
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“✅ Python Programming Books
✅ Coding Projects
✅ Important Pdfs
✅ Artificial Intelligence Courses
✅ Data Science Notes
For promotions: @love_data
Buy ads: https://telega.io/c/pythonproz”
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Kanal postlari
| 2 | Python Interview Questions for Data/Business Analysts:
Question 1:
Given a dataset in a CSV file, how would you read it into a Pandas DataFrame? And how would you handle missing values?
Question 2:
Describe the difference between a list, a tuple, and a dictionary in Python. Provide an example for each.
Question 3:
Imagine you are provided with two datasets, 'sales_data' and 'product_data', both in the form of Pandas DataFrames. How would you merge these datasets on a common column named 'ProductID'?
Question 4:
How would you handle duplicate rows in a Pandas DataFrame? Write a Python code snippet to demonstrate.
Question 5:
Describe the difference between '.iloc[] and '.loc[]' in the context of Pandas.
Question 6:
In Python's Matplotlib library, how would you plot a line chart to visualize monthly sales? Assume you have a list of months and a list of corresponding sales numbers.
Question 7:
How would you use Python to connect to a SQL database and fetch data into a Pandas DataFrame?
Question 8:
Explain the concept of list comprehensions in Python. Can you provide an example where it's useful for data analysis?
Question 9:
How would you reshape a long-format DataFrame to a wide format using Pandas? Explain with an example.
Question 10:
What are lambda functions in Python? How are they beneficial in data wrangling tasks?
Question 11:
Describe a scenario where you would use the 'groupby()' method in Pandas. How would you aggregate data after grouping?
Question 12:
You are provided with a Pandas DataFrame that contains a column with date strings. How would you convert this column to a datetime format? Additionally, how would you extract the month and year from these datetime objects?
Question 13:
Explain the purpose of the 'pivot_table' method in Pandas and describe a business scenario where it might be useful.
Question 14:
How would you handle large datasets that don't fit into memory? Are you familiar with Dask or any similar libraries?
Python Interview Q&A: https://topmate.io/coding/898340
Like for more ❤️
ENJOY LEARNING 👍👍 | 3 382 |
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| 4 | #ad | 1 856 |
| 5 |  🔰 Learn different methods to read text files in Python | 4 288 |
| 6 |  🔰 2 Types of Pythonistas | 4 016 |
| 7 | IntermediatePython.pdf | 4 306 |
| 8 | 𝗣𝗿𝗲𝗶𝗺𝗶𝗮𝗹 𝗣𝘆𝘁𝗵𝗼𝗻 𝗨𝗹𝘁𝗶𝗺𝗮𝘁𝗲 𝗚𝘂𝗶𝗱𝗲! 🚀🐍✨
𝗜𝗻𝗽𝘂𝘁/𝗢𝘂𝘁𝗽𝘂𝘁 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 📥📤
- print()
- input()
- format()
𝗗𝗮𝘁𝗮 𝗧𝘆𝗽𝗲 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗶𝗼𝗻 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 🔄
- int()
- float()
- str()
- bool()
- complex()
- list()
- tuple()
- set()
- dict()
- frozenset()
- bytes()
- bytearray()
- memoryview()
𝗠𝗮𝘁𝗵𝗲𝗺𝗮𝘁𝗶𝗰𝗮𝗹 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 🧮📐
- abs()
- pow()
- round()
- divmod()
- sum()
- min()
- max()
𝗦𝗲𝗾𝘂𝗲𝗻𝗰𝗲 & 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 📊📑
- len()
- sorted()
- range()
- zip()
- enumerate()
- reversed()
- all()
- any()
𝗧𝘆𝗽𝗲 & 𝗜𝗱𝗲𝗻𝘁𝗶𝘁𝘆 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 🔍🆔
- type()
- id()
- isinstance()
- issubclass()
𝗙𝗶𝗹𝗲 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 📂📝
- open()
- close()
- read()
- write()
- seek()
- tell()
𝗦𝘁𝗿𝗶𝗻𝗴 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 🔤🔠
- ord()
- chr()
- ascii()
- repr()
𝗨𝘁𝗶𝗹𝗶𝘁𝘆 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 🛠⚙️
- help()
- dir()
- eval()
- exec()
- hash()
𝗟𝗼𝗴𝗶𝗰𝗮𝗹 & 𝗕𝗶𝗻𝗮𝗿𝘆 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗶𝗼𝗻 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 🧠🔢
- bin()
- oct()
- hex()
- bool()
𝗠𝗲𝗺𝗼𝗿𝘆 & 𝗢𝗯𝗷𝗲𝗰𝘁 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 💾📦
- memoryview()
- object()
- callable()
#PythonGuide #PythonFunctions #CodingLife #LearnPython #DevCommunity #PyTips
https://t.me/pythonRe ✅ | 4 346 |
| 9 | 🧩 Local AI is no longer just a toy project.
In 2026, you can run a practical AI stack on a laptop: small LLMs, local embeddings, RAG, Jupyter/IDE integration, and no token bill.
This post breaks down what is actually usable right now: Qwen, Gemma, Llama, Ollama, Chroma/LanceDB, local RAG, Jupyter AI, hardware limits, and where local video still hurts.
Read the local stack | 1 388 |
| 10 | ✅ Python Data Types! 🐍✨
Data types define what kind of value a variable stores in Python.
name = "Python"
age = 25
price = 99.99
is_easy = True
1. String (str):
Used to store text values.
language = "Python"
city = 'Delhi'
✔ Written inside quotes "" or ''
✔ Used for names, messages, text data
2. Integer (int):
Used to store whole numbers.
age = 25
marks = 95
✔ No decimal point
✔ Positive or negative numbers allowed
3. Float (float):
Used to store decimal numbers.
price = 99.99
temperature = 36.6
✔ Numbers with decimal values
4. Boolean (bool):
Used for True or False values.
is_logged_in = True
is_admin = False
✔ Mostly used in conditions and comparisons
5. List (list):
Stores multiple values in one variable.
fruits = ["apple", "banana", "mango"]
✔ Ordered collection
✔ Can store duplicate values
✔ Uses square brackets []
6. Tuple (tuple):
Similar to list but cannot be changed.
colors = ("red", "blue", "green")
✔ Immutable unchangeable
✔ Uses parentheses ()
7. Set (set):
Stores unique values only.
nums = {1, 2, 3, 3, 4}
print(nums)
✔ Output → {1, 2, 3, 4}
✔ Removes duplicates automatically
8. Dictionary (dict):
Stores data in key-value pairs.
student = {
"name": "Alex",
"age": 22
}
✔ Uses curly braces {}
✔ Access values using keys
9. Check Data Type:
Use type() to check variable type.
name = "Python"
print(type(name))
✔ Output →
10. Type Conversion:
Convert one data type into another.
age = int("25")
price = float("99.5")
✔ int() → Integer
✔ float() → Decimal
✔ str() → String
11. Practice Examples:
✔ Add integers
a = 10
b = 20
print(a + b)
✔ Print list items
fruits = ["apple", "banana"]
print(fruits)
✔ Access dictionary value
student = {"name": "Alex"}
print(student["name"])
💡 Understanding data types is important because every Python program uses them.
💬 Tap ❤️ if this helped you! | 4 236 |
| 11 | Python Beginner Roadmap 🐍
📂 Start Here
∟📂 Install Python & VS Code
∟📂 Learn How to Run Python Files
📂 Python Basics
∟📂 Variables & Data Types
∟📂 Input & Output
∟📂 Operators (Arithmetic, Comparison)
∟📂 if, else, elif
∟📂 for & while loops
📂 Data Structures
∟📂 Lists
∟📂 Tuples
∟📂 Sets
∟📂 Dictionaries
📂 Functions
∟📂 Defining & Calling Functions
∟📂 Arguments & Return Values
📂 Basic File Handling
∟📂 Read & Write to Files (.txt)
📂 Practice Projects
∟📌 Calculator
∟📌 Number Guessing Game
∟📌 To-Do List (store in file)
📂 ✅ Move to Next Level (Only After Basics)
∟📂 Learn Modules & Libraries
∟📂 Small Real-World Scripts
For detailed explanation, join this channel 👇
https://whatsapp.com/channel/0029Vau5fZECsU9HJFLacm2a
React "❤️" For More :) | 4 073 |
| 12 |  15 Best Project Ideas for Python : 🐍
🚀 Beginner Level:
1. Simple Calculator
2. To-Do List
3. Number Guessing Game
4. Dice Rolling Simulator
5. Word Counter
🌟 Intermediate Level:
6. Weather App
7. URL Shortener
8. Movie Recommender System
9. Chatbot
10. Image Caption Generator
🌌 Advanced Level:
11. Stock Market Analysis
12. Autonomous Drone Control
13. Music Genre Classification
14. Real-Time Object Detection
15. Natural Language Processing (NLP) Sentiment Analysis | 3 740 |
| 13 | Complete roadmap to learn Python and Data Structures & Algorithms (DSA) in 2 months
### Week 1: Introduction to Python
Day 1-2: Basics of Python
- Python setup (installation and IDE setup)
- Basic syntax, variables, and data types
- Operators and expressions
Day 3-4: Control Structures
- Conditional statements (if, elif, else)
- Loops (for, while)
Day 5-6: Functions and Modules
- Function definitions, parameters, and return values
- Built-in functions and importing modules
Day 7: Practice Day
- Solve basic problems on platforms like HackerRank or LeetCode
### Week 2: Advanced Python Concepts
Day 8-9: Data Structures in Python
- Lists, tuples, sets, and dictionaries
- List comprehensions and generator expressions
Day 10-11: Strings and File I/O
- String manipulation and methods
- Reading from and writing to files
Day 12-13: Object-Oriented Programming (OOP)
- Classes and objects
- Inheritance, polymorphism, encapsulation
Day 14: Practice Day
- Solve intermediate problems on coding platforms
### Week 3: Introduction to Data Structures
Day 15-16: Arrays and Linked Lists
- Understanding arrays and their operations
- Singly and doubly linked lists
Day 17-18: Stacks and Queues
- Implementation and applications of stacks
- Implementation and applications of queues
Day 19-20: Recursion
- Basics of recursion and solving problems using recursion
- Recursive vs iterative solutions
Day 21: Practice Day
- Solve problems related to arrays, linked lists, stacks, and queues
### Week 4: Fundamental Algorithms
Day 22-23: Sorting Algorithms
- Bubble sort, selection sort, insertion sort
- Merge sort and quicksort
Day 24-25: Searching Algorithms
- Linear search and binary search
- Applications and complexity analysis
Day 26-27: Hashing
- Hash tables and hash functions
- Collision resolution techniques
Day 28: Practice Day
- Solve problems on sorting, searching, and hashing
### Week 5: Advanced Data Structures
Day 29-30: Trees
- Binary trees, binary search trees (BST)
- Tree traversals (in-order, pre-order, post-order)
Day 31-32: Heaps and Priority Queues
- Understanding heaps (min-heap, max-heap)
- Implementing priority queues using heaps
Day 33-34: Graphs
- Representation of graphs (adjacency matrix, adjacency list)
- Depth-first search (DFS) and breadth-first search (BFS)
Day 35: Practice Day
- Solve problems on trees, heaps, and graphs
### Week 6: Advanced Algorithms
Day 36-37: Dynamic Programming
- Introduction to dynamic programming
- Solving common DP problems (e.g., Fibonacci, knapsack)
Day 38-39: Greedy Algorithms
- Understanding greedy strategy
- Solving problems using greedy algorithms
Day 40-41: Graph Algorithms
- Dijkstra’s algorithm for shortest path
- Kruskal’s and Prim’s algorithms for minimum spanning tree
Day 42: Practice Day
- Solve problems on dynamic programming, greedy algorithms, and advanced graph algorithms
### Week 7: Problem Solving and Optimization
Day 43-44: Problem-Solving Techniques
- Backtracking, bit manipulation, and combinatorial problems
Day 45-46: Practice Competitive Programming
- Participate in contests on platforms like Codeforces or CodeChef
Day 47-48: Mock Interviews and Coding Challenges
- Simulate technical interviews
- Focus on time management and optimization
Day 49: Review and Revise
- Go through notes and previously solved problems
- Identify weak areas and work on them
### Week 8: Final Stretch and Project
Day 50-52: Build a Project
- Use your knowledge to build a substantial project in Python involving DSA concepts
Day 53-54: Code Review and Testing
- Refactor your project code
- Write tests for your project
Day 55-56: Final Practice
- Solve problems from previous contests or new challenging problems
Day 57-58: Documentation and Presentation
- Document your project and prepare a presentation or a detailed report
Day 59-60: Reflection and Future Plan
- Reflect on what you've learned
- Plan your next steps (advanced topics, more projects, etc.)
Best DSA RESOURCES: https://topmate.io/coding/886874
Credits: https://t.me/free4unow_backup
ENJOY LEARNING 👍👍 | 4 133 |
| 14 | Building Chatbots with Python | 4 066 |
| 15 | Found this - AI Builders, pay attention.
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Follow, like & share in "your network" - these guys are building something seriously worth watching.
PS: First systems go live tomorrow. Builders who join early get the best positioning... investor-backed marketing means they bring the clients to you. | 1 222 |
| 16 | Python Projects for your Data Science Portfolio
⚡️| Data Analysis Portfolio Projects
https://github.com/AlexTheAnalyst/PortfolioProjects
⚡️| Python for Data Analysis (pydata-book)
https://github.com/wesm/pydata-book
⚡️| Data Science Projects
https://github.com/CodeCutTech/Data-science
⚡️| End-to-End ML Projects
https://github.com/GokuMohandas/Made-With-ML
⚡️| Python Project Scripts
https://github.com/hastagAB/Awesome-Python-Scripts
⚡️| Applied ML in Production
https://github.com/eugeneyan/applied-ml
⚡️| Data Engineering Projects (Zoomcamp)
https://github.com/DataTalksClub/data-engineering-zoomcamp
⚡️| Real-Time Data Processing
https://github.com/andkret/Cookbook
⚡️| Plotly Dash Examples
https://github.com/plotly/dash-sample-apps
⚡️| Streamlit Gallery
https://github.com/streamlit/streamlit
⚡️| Web Scraping Projects
https://github.com/NirantK/awesome-project-ideas
⚡️| API Projects
https://github.com/public-apis/public-apis | 4 978 |
| 17 |  +7🔰 Python Statements | 4 364 |
| 18 | 📱 Python enthusiasts, this is for you — 15 BEST REPOSITORIES on GitHub for learning Python
▶️ Awesome Python — https://github.com/vinta/awesome-python
— the largest and most authoritative collection of frameworks, libraries, and resources for Python — a must-save
▶️ TheAlgorithms/Python — https://github.com/TheAlgorithms/Python
— a huge collection of algorithms and data structures written in Python
▶️ Project-Based-Learning — https://github.com/practical-tutorials/project-based-learning
— learning Python (and not only) through real projects
▶️ Real Python Guide — https://github.com/realpython/python-guide
— a high-quality guide to the Python ecosystem, tools, and best practices
▶️ Materials from Real Python — https://github.com/realpython/materials
— a collection of code and projects for Real Python articles and courses
▶️ Learn Python — https://github.com/trekhleb/learn-python
— a reference with explanations, examples, and exercises
▶️ Learn Python 3 — https://github.com/jerry-git/learn-python3
— a convenient guide to modern Python 3 with tasks
▶️ Python Reference — https://github.com/rasbt/python_reference
— cheat sheets, scripts, and useful tips from one of the most respected Python authors
▶️ 30-Days-Of-Python — https://github.com/Asabeneh/30-Days-Of-Python
— a 30-day challenge: from syntax to more complex topics
▶️ Python Programming Exercises — https://github.com/zhiwehu/Python-programming-exercises
— 100+ Python tasks with answers
▶️ Coding Problems — https://github.com/MTrajK/coding-problems
— tasks on algorithms and data structures, including for preparation for interviews
▶️ Projects — https://github.com/karan/Projects
— a list of ideas for pet projects (not just Python). Great for practice
▶️ 100-Days-Of-ML-Code — https://github.com/Avik-Jain/100-Days-Of-ML-Code
— machine learning in Python in the format of a challenge
▶️ 30-Seconds-of-Python — https://github.com/30-seconds/30-seconds-of-python
— useful snippets and tricks for everyday tasks
▶️ Geekcomputers/Python — https://github.com/geekcomputers/Python
— various scripts: from working with the network to automation tasks
React ♥️ for more posts like this 💛 | 4 034 |
| 19 |  🚀 New Edge for Polymarket Traders: Oracle Lag Sniper
A high-performance, open-source strategy repo is making waves right now among serious Polymarket users: the Oracle Lag Sniper.
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📊 Don’t just follow the market, get ahead of it. | 688 |
| 20 |  💻 Python Programming Roadmap
🔹 Stage 1: Python Basics (Syntax, Variables, Data Types)
🔹 Stage 2: Control Flow (if/else, loops)
🔹 Stage 3: Functions & Modules
🔹 Stage 4: Data Structures (Lists, Tuples, Sets, Dicts)
🔹 Stage 5: File Handling (Read/Write, CSV, JSON)
🔹 Stage 6: Error Handling (try/except, custom exceptions)
🔹 Stage 7: Object-Oriented Programming (Classes, Inheritance)
🔹 Stage 8: Standard Libraries (os, datetime, math)
🔹 Stage 9: Virtual Environments & pip package management
🔹 Stage 10: Working with APIs (Requests, JSON data)
🔹 Stage 11: Web Development Basics (Flask/Django)
🔹 Stage 12: Databases (SQLite, PostgreSQL, SQLAlchemy ORM)
🔹 Stage 13: Testing (unittest, pytest frameworks)
🔹 Stage 14: Version Control with Git & GitHub
🔹 Stage 15: Package Development (setup.py, publishing on PyPI)
🔹 Stage 16: Data Analysis (Pandas, NumPy libraries)
🔹 Stage 17: Data Visualization (Matplotlib, Seaborn)
🔹 Stage 18: Web Scraping (BeautifulSoup, Selenium)
🔹 Stage 19: Automation & Scripting projects
🔹 Stage 20: Advanced Topics (AsyncIO, Type Hints, Design Patterns)
💡 Tip: Master one stage before moving to the next. Build mini-projects to solidify your learning.
You can find detailed explanation here: 👇 https://whatsapp.com/channel/0029VbBDoisBvvscrno41d1l
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