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I’m launching a Python Projects Series — designed for beginners to those preparing for technical interviews or building real-world projects.

This will be a step-by-step, hands-on journey — where you’ll build useful Python projects with clear code, explanations, and mini-quizzes!

Here’s what we’ll cover:

🔹 Week 1: Python Mini Projects (Daily Practice)
⦁ Calculator
⦁ To-Do List (CLI)
⦁ Number Guessing Game
⦁ Unit Converter
⦁ Digital Clock

🔹 Week 2: Data Handling & APIs
⦁ Read/Write CSV & Excel files
⦁ JSON parsing
⦁ API Calls using Requests
⦁ Weather App using OpenWeather API
⦁ Currency Converter using Real-time API

🔹 Week 3: Automation with Python
⦁ File Organizer Script
⦁ Email Sender
⦁ WhatsApp Automation
⦁ PDF Merger
⦁ Excel Report Generator

🔹 Week 4: Data Analysis with Pandas & Matplotlib
⦁ Load & Clean CSV
⦁ Data Aggregation
⦁ Data Visualization
⦁ Trend Analysis
⦁ Dashboard Basics

🔹 Week 5: AI & ML Projects (Beginner Friendly)
⦁ Predict House Prices
⦁ Email Spam Classifier
⦁ Sentiment Analysis
⦁ Image Classification (Intro)
⦁ Basic Chatbot

📌 Each project includes: 
✅ Problem Statement 
✅ Code with explanation 
✅ Sample input/output 
✅ Learning outcome 
✅ Mini quiz

💬 React ❤️ if you're ready to build some projects together!

You can access it for free here
👇👇
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L

Let’s Build. Let’s Grow. 💻🙌

☑️

41. How do you handle categorical data? 
    Use encoding techniques like one-hot encoding (pd.get_dummies()), label encoding, or ordinal encoding to convert categories into numeric values.

42. Explain the difference between deep copy and shallow copy.
⦁ Shallow copy copies an object but references nested objects.
⦁ Deep copy copies everything recursively, creating independent objects.

43. What is the use of the enumerate() function? 
    Adds a counter to an iterable, yielding pairs (index, value) great for loops when you need the item index as well.

44. How do you detect and handle multicollinearity? 
    Use correlation matrix or Variance Inflation Factor (VIF). Handle by removing or combining correlated features.

45. How can you improve Python script performance? 
    Use efficient data structures, built-in functions, vectorized operations with NumPy/Pandas, and profile code to identify bottlenecks.

46. What are Python’s built-in data structures? 
    List, Tuple, Set, Dictionary, String.

47. How do you automate repetitive data tasks with Python? 
    Write scripts or use task schedulers (like cron/Windows Task Scheduler) with libraries such as pandas, openpyxl, and automation tools.

48. Explain the use of Assertions in Python. 
    Used for debugging by asserting conditions that must be true, raising errors if violated: 
    assert x > 0, "x must be positive"

49. How do you write unit tests in Python? 
    Use unittest or pytest frameworks to write test functions/classes that verify code behavior automatically.

50. How do you handle large datasets in Python? 
    Use chunking with Pandas read_csv(chunk_size=…), Dask for parallel computing, or databases to process data in parts rather than all at once.

Python Interview Questions: https://t.me/sqlspecialist/2220

React ♥️ if this helped you

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31. What are the differences between Python 2 and Python 3? 
    Python 3 introduced many improvements: print is a function (print()), better Unicode support, integer division changes, and removed deprecated features. Python 2 is now end-of-life.

32. How do you use regular expressions in Python? 
    With the re module, e.g., re.search(), re.findall(). They help match, search, or replace patterns in strings.

33. What is the purpose of the with statement? 
    Manages resources like file opening/closing automatically ensuring cleanup, e.g.,

with open('file.txt') as f:

    data = f.read()

34. Explain how to use virtual environments. 
    Isolate project dependencies using venv or virtualenv to avoid conflicts between package versions across projects.

35. How do you connect Python with SQL databases? 
    Using libraries like sqlite3, SQLAlchemy, or pymysql to execute SQL queries and fetch results into Python.

36. What is the role of the __init__.py file? 
    Marks a directory as a Python package and can initialize package-level code.

37. How do you handle JSON data in Python? 
    Use json module: json.load() to parse JSON files and json.dumps() to serialize Python objects to JSON.

38. What are generator functions and why use them? 
    Functions that yield values one at a time using yield, saving memory by lazy evaluation, ideal for large datasets.

39. How do you perform feature engineering with Python? 
    Create or transform variables using Pandas (e.g., creating dummy variables, extracting date parts), normalization, or combining features.

40. What is the purpose of the Pandas .pivot_table() method? 
    Creates spreadsheet-style pivot tables for summarizing data, allowing aggregation by multiple indices.

Double Tap ❤️ for Part-5