Key Projects

• Project 1: Simple Calculator – A command-line application that can perform basic arithmetic operations.
• Project 2: To-Do List Application – A user-friendly interface to manage daily tasks with features to add, delete, and view tasks.
• Project 3: Web Scraper – A script to extract and analyze data from websites, showcasing Python’s capabilities in data parsing.

Python Code Examples

Example Code for Simple Calculator

        
def calculator():
    operation = input("Select operation: +, -, *, /: ")
    num1 = float(input("Enter first number: "))
    num2 = float(input("Enter second number: "))

    if operation == '+':
        print(num1 + num2)
    elif operation == '-':
        print(num1 - num2)
    elif operation == '*':
        print(num1 * num2)
    elif operation == '/':
        print(num1 / num2)
    else:
        print("Invalid operation")

calculator()
        
    

Example Code for To-Do List Application

        
tasks = []

def add_task(task):
    tasks.append(task)
    print(f'Task "{task}" added.')

def show_tasks():
    print("Your To-Do List:")
    for task in tasks:
        print(f'- {task}')

while True:
    action = input("Enter 'add' to add a task, 'show' to see tasks, or 'quit' to exit: ")
    if action == 'add':
        task = input("What task would you like to add? ")
        add_task(task)
    elif action == 'show':
        show_tasks()
    elif action == 'quit':
        break
    else:
        print("Invalid input.")
        
    

Example Code for a Web Scraper

        
import requests
from bs4 import BeautifulSoup

def web_scraper(url):
    response = requests.get(url)
    soup = BeautifulSoup(response.text, 'html.parser')
    print(soup.title.string)

url = input("Enter the URL to scrape: ")
web_scraper(url)
        
    

Real-World Applications

Learning Python enables individuals to access numerous career opportunities across various fields. Here are some real-world applications:

• Web Development: Building web applications using frameworks like Flask or Django.
• Data Analysis: Analyzing complex datasets with libraries such as Pandas and NumPy.
• Machine Learning: Developing algorithms for AI using libraries like TensorFlow and scikit-learn.
• Automation: Automating repetitive tasks through scripting, increasing productivity.