Python Projects & Free Books
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Python Interview Projects & Free Courses

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Python Projects
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🔰 For Loop In Python
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- Location of Mobile Number Code -

import phonenumbers
from phonenumbers import timezone
from phonenumbers import geocoder
from phonenumbers import carrier

number = input("Enter the phone number with country code : ")

# Parsing String to the Phone number
phoneNumber = phonenumbers.parse(number)

# printing the timezone using the timezone module
timeZone = timezone.time_zones_for_number(phoneNumber)
print("timezone : "+str(timeZone))

# printing the geolocation of the given number using the geocoder module
geolocation = geocoder.description_for_number(phoneNumber,"en")
print("location : "+geolocation)

# printing the service provider name using the carrier module
service = carrier.name_for_number(phoneNumber,"en")
print("service provider : "+service)
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Convert any long article or PDF into a test in a couple of seconds!

Mini-service: we take the text of the article (or extract it from PDF), send it to GPT and receive a set of test questions with answer options and a key.

First, we load the text of the material:
# article_text — this is where we put the text of the article
with open("article.txt", "r", encoding="utf-8") as f:
    article_text = f.read()

# for PDF, you can extract the text in advance with any library (PyPDF2, pdfplumber, etc.)


Next, we ask GPT to generate a test:
prompt = (
    "You are an exam methodologist."
    "Based on this text, create 15 test questions."
    "Each question is in the format:\n"
    "1) Question text\n"
    "A. Option 1\n"
    "B. Option 2\n"
    "C. Option 3\n"
    "D. Option 4\n"
    "Correct answer: <letter>."
    "Do not add explanations and comments, only questions, options, and correct answers."
)
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": prompt},
        {"role": "user", "content": article_text}
    ])
print(response.choices[0].message.content.strip())


Suitable for online courses, educational centers, and corporate training — you immediately get a ready-made bank of tests from any article.
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📚 Python Libraries You Should Know

1. NumPy – Numerical computing
- Arrays, matrices, broadcasting
- Fast operations on large datasets
- Useful in data science & ML

2. Pandas – Data analysis & manipulation
- DataFrames and Series
- Reading/writing CSV, Excel
- GroupBy, filtering, merging

3. Matplotlib – Data visualization
- Line, bar, pie, scatter plots
- Custom styling & labels
- Save plots as images

4. Seaborn – Statistical plotting
- Built on Matplotlib
- Heatmaps, histograms, violin plots
- Great for EDA

5. Requests – HTTP library
- Make GET, POST requests
- Send headers, params, and JSON
- Used in web scraping and APIs

6. BeautifulSoup – Web scraping
- Parse HTML/XML easily
- Find elements using tags, class
- Navigate and extract data

7. Flask – Web development microframework
- Lightweight and fast
- Routes, templates, API building
- Great for small to medium apps

8. Django – High-level web framework
- Full-stack: ORM, templates, auth
- Scalable and secure
- Ideal for production-ready apps

9. SQLAlchemy – ORM for databases
- Abstract SQL queries in Python
- Connect to SQLite, PostgreSQL, etc.
- Schema creation & query chaining

10. Pytest – Testing framework
- Simple syntax for test cases
- Fixtures, asserts, mocking
- Supports plugins

11. Scikit-learn – Machine Learning
- Preprocessing, classification, regression
- Train/test split, pipelines
- Built on NumPy & Pandas

12. TensorFlow / PyTorch – Deep learning
- Neural networks, backpropagation
- GPU support
- Used in real AI projects

13. OpenCV – Computer vision
- Image processing, face detection
- Filters, contours, image transformations
- Real-time video analysis

14. Tkinter – GUI development
- Build desktop apps
- Buttons, labels, input fields
- Easy drag-and-drop interface

Credits: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1885

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Python Roadmap 🐍 (Beginner → Job)

📂 Syntax Basics
∟ Variables, Data Types
∟ Conditions & Loops

📂 Data Structures
∟ List, Tuple, Set, Dict
∟ Comprehensions

📂 Algorithms
∟ Searching & Sorting
∟ Recursion & Big-O

📂 OOP Concepts
∟ Class & Object
∟ Inheritance & Polymorphism

📂 Modules & Errors
∟ Import & pip
∟ try / except

📂 File Handling
∟ Read / Write Files
∟ CSV & JSON

📂 Networking
∟ APIs & Requests
∟ JSON Data

📂 Security Basics
∟ Password Hashing
∟ API Keys

📂 Practice & Projects
∟ Mini Programs
∟ Real Projects

Job / Internship Ready

❤️ React for more Python roadmaps
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Kandinsky 5.0 Video Lite and Kandinsky 5.0 Video Pro generative models on the global text-to-video landscape

🔘Pro is currently the #1 open-source model worldwide
🔘Lite (2B parameters) outperforms Sora v1.
🔘Only Google (Veo 3.1, Veo 3), OpenAI (Sora 2), Alibaba (Wan 2.5), and KlingAI (Kling 2.5, 2.6) outperform Pro — these are objectively the strongest video generation models in production today. We are on par with Luma AI (Ray 3) and MiniMax (Hailuo 2.3): the maximum ELO gap is 3 points, with a 95% CI of ±21.

Useful links
🔘Full leaderboard: LM Arena
🔘Kandinsky 5.0 details: technical report
🔘Open-source Kandinsky 5.0: GitHub and Hugging Face
API Key Authentication

import requests

# API endpoint
url = "https://api.example.com/data"

# Parameters including the API key for authentication
params = {
    "api_key": "YOUR_API_KEY"  # Replace with your actual API key
}

# Send GET request with parameters
response = requests.get(url, params=params)

# Convert JSON response to Python object
data = response.json()

# Print the data
print(data)


Next up ➡️ Importing Pickle files in python
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🧠 DSA with Python Roadmap (Beginner → Interview)

📂 DSA Foundations
∟ What is DSA
∟ Time & Space Complexity

📂 Python for DSA
∟ Lists & Strings
∟ Set & Dictionary Usage

📂 Searching Algorithms
∟ Linear Search
∟ Binary Search

📂 Sorting Algorithms
∟ Bubble, Selection, Insertion
∟ Merge & Quick Sort

📂 Recursion Basics
∟ Base & Recursive Case
∟ Stack Memory

📂 Stack & Queue
∟ Stack using List
∟ Queue using Deque

📂 Linked List
∟ Singly & Doubly Linked List
∟ Slow & Fast Pointer

📂 Problem-Solving Patterns
∟ Two Pointer
∟ Sliding Window

📂 Hashing Techniques
∟ Frequency Count
∟ Subarray Problems

📂 Trees
∟ Binary Tree
∟ Tree Traversals

📂 Heap & Priority Queue
∟ Min / Max Heap
∟ Top-K Problems

📂 Graphs
∟ BFS & DFS
∟ Cycle Detection

📂 Dynamic Programming
∟ Memoization
∟ Tabulation

📂 Bit Manipulation
∟ Bitwise Operators
∟ Common Tricks

📂 Practice Strategy
∟ Beginner → Advanced Problems
∟ Revision & Mock Interviews

Interview Ready

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🔰 Python list methods
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Python Coding Interview Questions 🐍💻

1️⃣ Q: Return the first duplicate in a list.
def first_duplicate(lst):
seen = set()
for x in lst:
if x in seen:
return x
seen.add(x)
return None

print(first_duplicate([3, 1, 3, 4, 2])) # Output: 3

2️⃣ Q: Check whether a number is a palindrome.
def is_pal_num(n):
return str(n) == str(n)[::-1]

print(is_pal_num(121)) # True
print(is_pal_num(123)) # False

3️⃣ Q: Sort a dictionary by values.
def sort_by_value(d):
return dict(sorted(d.items(), key=lambda x: x[1]))

print(sort_by_value({'a': 3, 'b': 1, 'c': 2}))
Output: {'b': 1, 'c': 2, 'a': 3}

4️⃣ Q: Return all prime numbers in a given range.
def primes_upto(n):
primes = []
for num in range(2, n + 1):
for i in range(2, int(num**0.5) + 1):
if num % i == 0:
break
else:
primes.append(num)
return primes

print(primes_upto(10)) # [2, 3, 5, 7]

5️⃣ Q: Convert a list of numbers into a string.
def list_to_string(lst):
return "".join(map(str, lst))

print(list_to_string([1, 2, 3])) # Output: 123

💬 Double Tap ❤️ for Part-12
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📖 Data Science Packages
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Essential Python and SQL topics for data analysts 😄👇

Python Topics:

1. Data Structures
   - Lists, Tuples, and Dictionaries
   - NumPy Arrays for numerical data

2. Data Manipulation
   - Pandas DataFrames for structured data
   - Data Cleaning and Preprocessing techniques
   - Data Transformation and Reshaping

3. Data Visualization
   - Matplotlib for basic plotting
   - Seaborn for statistical visualizations
   - Plotly for interactive charts

4. Statistical Analysis
   - Descriptive Statistics
   - Hypothesis Testing
   - Regression Analysis

5. Machine Learning
   - Scikit-Learn for machine learning models
   - Model Building, Training, and Evaluation
   - Feature Engineering and Selection

6. Time Series Analysis
   - Handling Time Series Data
   - Time Series Forecasting
   - Anomaly Detection

7. Python Fundamentals
   - Control Flow (if statements, loops)
   - Functions and Modular Code
   - Exception Handling
   - File

SQL Topics:

1. SQL Basics
- SQL Syntax
- SELECT Queries
- Filters

2. Data Retrieval
- Aggregation Functions (SUM, AVG, COUNT)
- GROUP BY

3. Data Filtering
- WHERE Clause
- ORDER BY

4. Data Joins
- JOIN Operations
- Subqueries

5. Advanced SQL
- Window Functions
- Indexing
- Performance Optimization

6. Database Management
- Connecting to Databases
- SQLAlchemy

7. Database Design
- Data Types
- Normalization

Remember, it's highly likely that you won't know all these concepts from the start. Data analysis is a journey where the more you learn, the more you grow. Embrace the learning process, and your skills will continually evolve and expand. Keep up the great work!

Python Resources - https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L

SQL Resources - https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v

Hope it helps :)
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Here is the list of few projects (found on kaggle). They cover Basics of Python, Advanced Statistics, Supervised Learning (Regression and Classification problems) & Data Science

Please also check the discussions and notebook submissions for different approaches and solution after you tried yourself.

1. Basic python and statistics

Pima Indians :- https://www.kaggle.com/uciml/pima-indians-diabetes-database
Cardio Goodness fit :- https://www.kaggle.com/saurav9786/cardiogoodfitness
Automobile :- https://www.kaggle.com/toramky/automobile-dataset

2. Advanced Statistics

Game of Thrones:-https://www.kaggle.com/mylesoneill/game-of-thrones
World University Ranking:-https://www.kaggle.com/mylesoneill/world-university-rankings
IMDB Movie Dataset:- https://www.kaggle.com/carolzhangdc/imdb-5000-movie-dataset

3. Supervised Learning

a) Regression Problems

How much did it rain :- https://www.kaggle.com/c/how-much-did-it-rain-ii/overview
Inventory Demand:- https://www.kaggle.com/c/grupo-bimbo-inventory-demand
Property Inspection predictiion:- https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction
Restaurant Revenue prediction:- https://www.kaggle.com/c/restaurant-revenue-prediction/data
IMDB Box office Prediction:-https://www.kaggle.com/c/tmdb-box-office-prediction/overview

b) Classification problems

Employee Access challenge :- https://www.kaggle.com/c/amazon-employee-access-challenge/overview
Titanic :- https://www.kaggle.com/c/titanic
San Francisco crime:- https://www.kaggle.com/c/sf-crime
Customer satisfcation:-https://www.kaggle.com/c/santander-customer-satisfaction
Trip type classification:- https://www.kaggle.com/c/walmart-recruiting-trip-type-classification
Categorize cusine:- https://www.kaggle.com/c/whats-cooking

4. Some helpful Data science projects for beginners

https://www.kaggle.com/c/house-prices-advanced-regression-techniques

https://www.kaggle.com/c/digit-recognizer

https://www.kaggle.com/c/titanic

5. Intermediate Level Data science Projects

Black Friday Data : https://www.kaggle.com/sdolezel/black-friday

Human Activity Recognition Data : https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones

Trip History Data : https://www.kaggle.com/pronto/cycle-share-dataset

Million Song Data : https://www.kaggle.com/c/msdchallenge

Census Income Data : https://www.kaggle.com/c/census-income/data

Movie Lens Data : https://www.kaggle.com/grouplens/movielens-20m-dataset

Twitter Classification Data : https://www.kaggle.com/c/twitter-sentiment-analysis2

Share with credits: https://t.me/sqlproject

ENJOY LEARNING 👍👍
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Closures & Decorators in Python 👆
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