Python Programming & AI Resources
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ā Python Programming Books ā Coding Projects ā Important Pdfs ā Artificial Intelligence Courses ā Data Science Notes For promotions: @love_data Buy ads: https://telega.io/c/pythonproz
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Network:š Analytical overview of Telegram channel Python Programming & AI Resources
Channel Python Programming & AI Resources (@pythonproz) in the English language segment is an active participant. Currently, the community unites 13 523 subscribers, ranking 9 361 in the Technologies & Applications category and 29 219 in the India region.
š Audience metrics and dynamics
Since its creation on Š½ŠµŠ²ŃŠ“Š¾Š¼Š¾, the project has demonstrated rapid growth, gathering an audience of 13 523 subscribers.
According to the latest data from 25 October, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -81 over the last 30 days and by -1 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 19.05%. Within the first 24 hours after publication, content typically collects N/A% reactions from the total number of subscribers.
- Post reach: On average, each post receives 0 views. Within the first day, a publication typically gains 0 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 0.
- Thematic interests: Content is focused on key topics such as tuple, comprehension, learning, programming, loop.
š Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
āā
Python Programming Books
ā
Coding Projects
ā
Important Pdfs
ā
Artificial Intelligence Courses
ā
Data Science Notes
For promotions: @love_data
Buy ads: https://telega.io/c/pythonprozā
Thanks to the high frequency of updates (latest data received on 26 October, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.
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Channel Posts
ā
Python Checklist for Data Analysts š§
1. Python Basics
āŖ Variables, data types (int, float, str, bool)
āŖ Control flow: if-else, loops (for, while)
āŖ Functions and lambda expressions
āŖ List, dict, tuple, set basics
2. Data Handling & Manipulation
āŖ NumPy: arrays, vectorized operations, broadcasting
āŖ Pandas: Series & DataFrame, reading/writing CSV, Excel
āŖ Data inspection:
head(), info(), describe()
āŖ Filtering, sorting, grouping (groupby), merging/joining datasets
āŖ Handling missing data (isnull(), fillna(), dropna())
3. Data Visualization
āŖ Matplotlib basics: plots, histograms, scatter plots
āŖ Seaborn: statistical visualizations (heatmaps, boxplots)
āŖ Plotly (optional): interactive charts
4. Statistics & Probability
āŖ Descriptive stats (mean, median, std)
āŖ Probability distributions, hypothesis testing (SciPy, statsmodels)
āŖ Correlation, covariance
5. Working with APIs & Data Sources
āŖ Fetching data via APIs (requests library)
āŖ Reading JSON, XML
āŖ Web scraping basics (BeautifulSoup, Scrapy)
6. Automation & Scripting
āŖ Automate repetitive data tasks using loops, functions
āŖ Excel automation (openpyxl, xlrd)
āŖ File handling and regular expressions
7. Machine Learning Basics (Optional starting point)
āŖ Scikit-learn for basic models (regression, classification)
āŖ Train-test split, evaluation metrics
8. Version Control & Collaboration
āŖ Git basics: init, commit, push, pull
āŖ Sharing notebooks or scripts via GitHub
9. Environment & Tools
āŖ Jupyter Notebook / JupyterLab for interactive analysis
āŖ Python IDEs (VSCode, PyCharm)
āŖ Virtual environments (venv, conda)
10. Projects & Portfolio
āŖ Analyze real datasets (Kaggle, UCI)
āŖ Document insights in notebooks or blogs
āŖ Showcase code & analysis on GitHub
š” Tips:
ā¦ Practice coding daily with mini-projects and challenges
ā¦ Use interactive platforms like Kaggle, DataCamp, or LeetCode (Python)
ā¦ Combine SQL + Python skills for powerful data querying & analysis
Python Programming Resources: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
Double Tap ā„ļø For More| 2 | ![#Ad #AI_Models š„ GigaChat 3.5 Reasoning [Open-Source] ā¹ļø Overview: New LLM that thinks before it answers. Breaks problems in](https://cdn.tlmtr.io/_next/image?url=https%3A%2F%2Fn1.tlmtr.cc%2Fv%2F_u74IP8VHJ7vIO8AuGlc4bjQucvvMGYIZhPDTZs5S5So%3Fty%3Dx&w=64&q=75){kind=small} #Ad
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ā¹ļø Overview:
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š Source:
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š Model Specs:
āŖ Built on GigaChat 3.5 Ultra with multiple step-by-step reasoning paths
āŖ Proprietary linear attention for efficient long contexts
āŖ Token-efficient: 37% fewer tokens than DeepSeek V4 Flash Preview
āŖ Benchmarks: IFBench 44ā77, Natural Plan 64ā80, LiveCodeBench v6 56ā85
āŖ MIT License | 1 797 |
| 3 |  +6Double Tap ā¤ļø For More Python Notes | 1 968 |
| 4 | 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 ā¤ļø
ENJOY LEARNING šš | 3 104 |
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| 6 | #ad | 1 856 |
| 7 |  š° Learn different methods to read text files in Python | 4 288 |
| 8 |  š° 2 Types of Pythonistas | 4 016 |
| 9 | IntermediatePython.pdf | 4 306 |
| 10 | š£šæš²š¶šŗš¶š®š¹ š£šššµš¼š» šØš¹šš¶šŗš®šš² ššš¶š±š²! ššāØ
šš»š½šš/š¢ššš½šš ššš»š°šš¶š¼š»š š„š¤
- print()
- input()
- format()
šš®šš® š§šš½š² šš¼š»šš²šæšš¶š¼š» ššš»š°šš¶š¼š»š š
- int()
- float()
- str()
- bool()
- complex()
- list()
- tuple()
- set()
- dict()
- frozenset()
- bytes()
- bytearray()
- memoryview()
š š®ššµš²šŗš®šš¶š°š®š¹ ššš»š°šš¶š¼š»š š§®š
- abs()
- pow()
- round()
- divmod()
- sum()
- min()
- max()
š¦š²š¾šš²š»š°š² & šš¼š¹š¹š²š°šš¶š¼š» ššš»š°šš¶š¼š»š šš
- len()
- sorted()
- range()
- zip()
- enumerate()
- reversed()
- all()
- any()
š§šš½š² & šš±š²š»šš¶šš ššš»š°šš¶š¼š»š šš
- type()
- id()
- isinstance()
- issubclass()
šš¶š¹š² šš®š»š±š¹š¶š»š“ ššš»š°šš¶š¼š»š šš
- open()
- close()
- read()
- write()
- seek()
- tell()
š¦ššæš¶š»š“ ššš»š°šš¶š¼š»š š¤š
- ord()
- chr()
- ascii()
- repr()
šØšš¶š¹š¶šš ššš»š°šš¶š¼š»š š āļø
- help()
- dir()
- eval()
- exec()
- hash()
šš¼š“š¶š°š®š¹ & šš¶š»š®šæš šš¼š»šš²šæšš¶š¼š» ššš»š°šš¶š¼š»š š§ š¢
- bin()
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š š²šŗš¼šæš & š¢šÆš·š²š°š šš®š»š±š¹š¶š»š“ ššš»š°šš¶š¼š»š š¾š¦
- memoryview()
- object()
- callable()
#PythonGuide #PythonFunctions #CodingLife #LearnPython #DevCommunity #PyTips
https://t.me/pythonRe ā
| 4 346 |
| 11 | š§© 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 |
| 12 | ā
Python Data Types! šāØ
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'
ā Written inside quotes "" or ''
ā Used for names, messages, text data
2. Integer (int):
Used to store whole numbers.
age = 25
marks = 95
ā No decimal point
ā Positive or negative numbers allowed
3. Float (float):
Used to store decimal numbers.
price = 99.99
temperature = 36.6
ā Numbers with decimal values
4. Boolean (bool):
Used for True or False values.
is_logged_in = True
is_admin = False
ā Mostly used in conditions and comparisons
5. List (list):
Stores multiple values in one variable.
fruits = ["apple", "banana", "mango"]
ā Ordered collection
ā Can store duplicate values
ā Uses square brackets []
6. Tuple (tuple):
Similar to list but cannot be changed.
colors = ("red", "blue", "green")
ā Immutable unchangeable
ā Uses parentheses ()
7. Set (set):
Stores unique values only.
nums = {1, 2, 3, 3, 4}
print(nums)
ā Output ā {1, 2, 3, 4}
ā Removes duplicates automatically
8. Dictionary (dict):
Stores data in key-value pairs.
student = {
"name": "Alex",
"age": 22
}
ā Uses curly braces {}
ā Access values using keys
9. Check Data Type:
Use type() to check variable type.
name = "Python"
print(type(name))
ā Output ā
10. Type Conversion:
Convert one data type into another.
age = int("25")
price = float("99.5")
ā int() ā Integer
ā float() ā Decimal
ā str() ā String
11. Practice Examples:
ā Add integers
a = 10
b = 20
print(a + b)
ā Print list items
fruits = ["apple", "banana"]
print(fruits)
ā Access dictionary value
student = {"name": "Alex"}
print(student["name"])
š” Understanding data types is important because every Python program uses them.
š¬ Tap ā¤ļø if this helped you! | 4 236 |
| 13 | Python Beginner Roadmap š
š Start Here
āš Install Python & VS Code
āš Learn How to Run Python Files
š Python Basics
āš Variables & Data Types
āš Input & Output
āš Operators (Arithmetic, Comparison)
āš if, else, elif
āš for & while loops
š Data Structures
āš Lists
āš Tuples
āš Sets
āš Dictionaries
š Functions
āš Defining & Calling Functions
āš Arguments & Return Values
š Basic File Handling
āš Read & Write to Files (.txt)
š Practice Projects
āš Calculator
āš Number Guessing Game
āš To-Do List (store in file)
š ā
Move to Next Level (Only After Basics)
āš Learn Modules & Libraries
āš Small Real-World Scripts
For detailed explanation, join this channel š
https://whatsapp.com/channel/0029Vau5fZECsU9HJFLacm2a
React "ā¤ļø" For More :) | 4 073 |
| 14 |  15 Best Project Ideas for Python : š
š Beginner Level:
1. Simple Calculator
2. To-Do List
3. Number Guessing Game
4. Dice Rolling Simulator
5. Word Counter
š Intermediate Level:
6. Weather App
7. URL Shortener
8. Movie Recommender System
9. Chatbot
10. Image Caption Generator
š 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 |
| 15 | 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
- Dijkstraās algorithm for shortest path
- Kruskalās and Primās 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 šš | 4 133 |
| 16 | Building Chatbots with Python | 4 066 |
| 17 | Found this - AI Builders, pay attention.
A curated marketplace just launched where AI builders list their systems and get paid - setup fee + monthly recurring. No sales, no client chasing. They handle everything, you just build.
100% free to join. No fees, no subscription, no hidden costs. They only take 20% when you earn - on setup fee and recurring. That's it.
Accepted builders are earning from day one. Spots are limited by design.
Takes 5 minutes to apply. You'll need a 90-second video of your system in action.
ā https://tglink.io/3508aa8711f389
Daily updates from the CEO: https://tglink.io/e2ba74d53c7039
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 |
| 18 | Python Projects for your Data Science Portfolio
ā”ļø| Data Analysis Portfolio Projects
https://github.com/AlexTheAnalyst/PortfolioProjects
ā”ļø| Python for Data Analysis (pydata-book)
https://github.com/wesm/pydata-book
ā”ļø| Data Science Projects
https://github.com/CodeCutTech/Data-science
ā”ļø| End-to-End ML Projects
https://github.com/GokuMohandas/Made-With-ML
ā”ļø| Python Project Scripts
https://github.com/hastagAB/Awesome-Python-Scripts
ā”ļø| Applied ML in Production
https://github.com/eugeneyan/applied-ml
ā”ļø| Data Engineering Projects (Zoomcamp)
https://github.com/DataTalksClub/data-engineering-zoomcamp
ā”ļø| Real-Time Data Processing
https://github.com/andkret/Cookbook
ā”ļø| Plotly Dash Examples
https://github.com/plotly/dash-sample-apps
ā”ļø| Streamlit Gallery
https://github.com/streamlit/streamlit
ā”ļø| Web Scraping Projects
https://github.com/NirantK/awesome-project-ideas
ā”ļø| API Projects
https://github.com/public-apis/public-apis | 4 978 |
| 19 |  +7š° Python Statements | 4 364 |
| 20 | š± Python enthusiasts, this is for you ā 15 BEST REPOSITORIES on GitHub for learning Python
ā¶ļø Awesome Python ā https://github.com/vinta/awesome-python
ā the largest and most authoritative collection of frameworks, libraries, and resources for Python ā a must-save
ā¶ļø TheAlgorithms/Python ā https://github.com/TheAlgorithms/Python
ā a huge collection of algorithms and data structures written in Python
ā¶ļø Project-Based-Learning ā https://github.com/practical-tutorials/project-based-learning
ā learning Python (and not only) through real projects
ā¶ļø Real Python Guide ā https://github.com/realpython/python-guide
ā a high-quality guide to the Python ecosystem, tools, and best practices
ā¶ļø Materials from Real Python ā https://github.com/realpython/materials
ā a collection of code and projects for Real Python articles and courses
ā¶ļø Learn Python ā https://github.com/trekhleb/learn-python
ā a reference with explanations, examples, and exercises
ā¶ļø Learn Python 3 ā https://github.com/jerry-git/learn-python3
ā a convenient guide to modern Python 3 with tasks
ā¶ļø Python Reference ā https://github.com/rasbt/python_reference
ā cheat sheets, scripts, and useful tips from one of the most respected Python authors
ā¶ļø 30-Days-Of-Python ā https://github.com/Asabeneh/30-Days-Of-Python
ā a 30-day challenge: from syntax to more complex topics
ā¶ļø Python Programming Exercises ā https://github.com/zhiwehu/Python-programming-exercises
ā 100+ Python tasks with answers
ā¶ļø Coding Problems ā https://github.com/MTrajK/coding-problems
ā tasks on algorithms and data structures, including for preparation for interviews
ā¶ļø Projects ā https://github.com/karan/Projects
ā a list of ideas for pet projects (not just Python). Great for practice
ā¶ļø 100-Days-Of-ML-Code ā https://github.com/Avik-Jain/100-Days-Of-ML-Code
ā machine learning in Python in the format of a challenge
ā¶ļø 30-Seconds-of-Python ā https://github.com/30-seconds/30-seconds-of-python
ā useful snippets and tricks for everyday tasks
ā¶ļø Geekcomputers/Python ā https://github.com/geekcomputers/Python
ā various scripts: from working with the network to automation tasks
React ā„ļø for more posts like this š | 4 034 |
