Python Datascience

Python is a simple, easy-to-learn programming language that’s perfect for beginners. It uses clean, readable syntax that feels like writing plain English, making it a great first language for anyone new to coding.

In the beginning, you’ll learn how to install Python on your computer, explore popular code editors like VS Code and PyCharm, and write your very first Python program.

You’ll also understand how Python code runs, how to use the Python interpreter, and get comfortable with basic tools that make coding fun and efficient.

This foundation sets you up for all the exciting things you’ll build with Python!

📚 Table of Contents

1. Introduction to Python
2. Basic Syntax and Operations
3. Control Flow
4. Data Structures
5. Functions
6. Object-Oriented Programming (OOP)
7. File Handling
8. Error Handling
9. Modules and Packages
10. Advanced Topics
11. Data Science Libraries
12. Web Scraping
13. APIs and JSON
14. Visual Studio Code
15. Google Colab
16. PIP and Virtual Environment
17. Coding with AI
18. Python Project 1: Personal Book Manager
19. Python Project 2: Quote Data Explorer

1. Introduction to Python

• Overview of Python
• Installation and Setup
• Python IDEs (PyCharm, VSCode, etc.)
• Writing and Running Your First Python Program

2. Basic Syntax and Operations

• Variables and Data Types
• Basic Input and Output
• Arithmetic, Comparison, and Logical Operators
• Python Comments and Documentation

3. Control Flow

• Conditional Statements (if, elif, else)
• Looping Constructs (for, while)
• break, continue, pass Statements

4. Data Structures

Lists

• Creating, Accessing, and Modifying Lists
• List Methods

Tuples

• Differences between Lists and Tuples

Dictionaries

• Key-Value Pairs, Common Methods

Sets

• Creating Sets, Set Operations

5. Functions

• Defining and Calling Functions
• Function Arguments (positional, keyword, default)
• Return Statements
• Lambda Functions
• Scope of Variables (local, global)

6. Object-Oriented Programming (OOP)

• Introduction to Classes and Objects
• Constructors and Destructors
• Class and Instance Variables
• Methods (instance, class, static)
• Inheritance, Polymorphism, and Encapsulation
• Magic Methods and Operator Overloading

7. File Handling

• Reading and Writing Text Files
• File Methods (open, read, write, close)
• Working with CSV Files

8. Error Handling

• Types of Errors (syntax, runtime)
• Exception Handling (try, except, finally)
• Custom Exceptions

9. Modules and Packages

• Importing Built-in and External Modules
• Creating Custom Modules
• Package Structure and Importing

10. Advanced Topics

• List Comprehensions
• Generators and Iterators
• Decorators
• Context Managers (with statement)

11. Data Science Libraries

• NumPy Basics
• Pandas for Data Manipulation
• Matplotlib for Basic Plotting

12. Web Scraping

• Using BeautifulSoup and Requests
• Extracting Data from HTML

13. APIs and JSON

• Introduction to REST APIs
• Making API Requests
• Working with JSON Data

14. Visual Studio Code

• Installing and Setting Up VS Code
• Creating and Running Python Files
• Visual Studio Code Plugins

15. Google Colab

• What is Google Colab?
• Using Google Colab for Python Programming
• Uploading and Downloading Files
• Running Code in the Cloud
• Sharing and Collaboration Features

16. PIP and Virtual Environment

• Introduction to pip
• Installing Packages using pip
• Upgrading and Uninstalling Packages
• What is a Virtual Environment?
• Creating a Virtual Environment with venv
• Activating and Deactivating the Environment
• Managing Dependencies with requirements.txt

17. Coding with AI

What is GitHub Copilot?

• Overview and capabilities
• Powered by OpenAI Codex
• Not a replacement for critical thinking
• Meant to assist, not automate everything

Setup and Configuration

• Install GitHub Copilot extension in VS Code or JetBrains IDEs
• Log in using a GitHub account
• Activate GitHub Copilot via command palette
• Configure:
• Enable/disable globally or per language
• Control number of suggestions
• Use inline or block completions

Writing Code with Copilot

• Use meaningful comments to guide suggestions
• Start typing function names or docstrings
• Copilot responds well to context and structure
• Basic functions: factorial, reverse string, Fibonacci
• Loop generation: for, while
• List/dictionary operations
• Class definitions (constructor, methods, inheritance)
• Simple REST API with Flask

Reading and Evaluating Suggestions

• Cycle through suggestions (Tab, Ctrl+[ / ])
• Accept or reject code
• Edit and verify logic
• Always test for:
• Correctness
• Efficiency
• Readability

Enhancing Productivity with Copilot

• Reduce boilerplate (e.g., Flask routes, Pandas configs)
• Write tests (unittest, pytest)
• Auto-generate docstrings and type hints
• Create sample data (lists, dicts, JSON)
• Help with regex, SQL queries, file handling

Best Practices

• Use descriptive comments to guide suggestions
• Keep code clean and readable
• Use Copilot for ideas, not answers
• Regularly refactor and optimize generated code
• Cross-check with documentation
• Do not use Copilot for sensitive or proprietary code without reviewing

Use Cases by Domain

Limitations and Ethics

• May hallucinate functions or imports
• Can generate insecure or incorrect code
• May include code fragments from training data
• Risk of copyright or licensing issues
• Requires human oversight and responsibility

Comparison with Other AI Tools (Optional)

Practice Activities

• Complete partially written functions
• Add Copilot to a sample project and document suggestions
• Create multiple variations of the same function
• Refactor Copilot-generated code for readability
• Generate test cases for a given module
• Compare Copilot suggestion vs official documentation

Final Tips

• Encourage: “Why did Copilot suggest this?”
• Blend Copilot with Google, StackOverflow, and documentation
• Use debugging tasks to identify Copilot’s flaws
• Foster exploration and creativity through code prompts
• Remind: Copilot is a collaborator, not a crutch

18. Python Project 1: Personal Book Manager (CLI-Based)

🎯 Objective

Build a command-line Python application to manage a personal library of books.

📦 Concepts Covered

• Variables, I/O, Lists, Dictionaries
• Functions, File Handling
• Exception Handling
• CSV and JSON
• Decorators, Generators
• OOP (Optional)

🔧 Requirements

1. User Authentication

• Accept username/password (hardcoded or stored)

2. Menu Options

• Add, View, Search, Update, Delete, Export, Exit

3. Data Storage

books = [
    {"title": "Python 101", "author": "John Doe", "genre": "Education", "rating": 4.5},
]

4. File Handling

• Load from JSON on start
• Save to JSON on exit

5. Functions

• Use a separate function for each operation

6. CSV Export

• Use csv module

7. Optional OOP

• Create a Book class

8. Bonus

• Logging with decorators
• Book generator

🧪 Sample Output

Welcome to Personal Book Manager!
1. Add Book
2. View All
3. Search
4. Update Rating
5. Delete Book
6. Export
7. Exit
Choose an option:

19. Python Project 2: Quote Data Explorer

🎯 Objective

Scrape quotes from the web and perform data analysis using Pandas and NumPy. This project blends web scraping with real-world data handling tasks.

📦 Concepts Covered

• Web Scraping using requests, BeautifulSoup
• DataFrames using Pandas
• Array operations with NumPy
• Data Filtering, Grouping, Transformation
• File I/O (CSV and JSON)
• Error Handling and Menu-driven CLI

🔧 Project Requirements

1. Data Source

• Scrape quotes from: https://quotes.toscrape.com
• Limit to the first 5 pages

2. Extracted Fields

For each quote, extract:

• quote (text)
• author
• tags (comma-separated string or list)

3. Data Storage

• Store data in a Pandas DataFrame
• Each row should represent one quote
• Columns: quote, author, tags

🗂️ Menu Options (CLI)

Welcome to Quote Data Explorer!
1. View Sample Data
2. Show Unique Authors
3. Filter by Author
4. Filter by Tag
5. Tag Frequency (Top N)
6. Export to CSV/JSON
7. Exit
Choose an option: