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Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida taāriflaydi:
āā
Python Programming Books
ā
Coding Projects
ā
Important Pdfs
ā
Artificial Intelligence Courses
ā
Data Science Notes
For promotions: @love_data
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Kanal postlari
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
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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()
- oct()
- hex()
- bool()
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.
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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
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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 |
| 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 |
