1. Creating a DataFrame from Travel Package Data

destination = ["Bali", "Dubai", "Paris", "Ladakh"]
price = [45000, 60000, 120000, 35000]
duration_days = [5, 4, 7, 6]


Create a DataFrame from the above lists and print the first two rows.

2. Creating a DataFrame from List of Lists (Stock Prices)

data = [
 ["INFY", 1510, 1528],
 ["TCS", 3550, 3610],
 ["RELIANCE", 2450, 2430]
]


df = pd.DataFrame({
    "product": ["Laptop", "Mobile", "Tablet", "Laptop"],
    "units": [10, 25, 5, 8],
    "price": [55000, 22000, 15000, 56000]
})


employees = pd.DataFrame({
    "name": ["Amit", "Riya", "John", "Pooja"],
    "department": ["IT", "HR", "Finance", "Sales"],
    "salary": [55000, 48000, 62000, 45000],
    "city": ["Mumbai", "Delhi", "Pune", "Mumbai"]
})


5. Subsetting Rows – Head & Slicing (Student Scores)

students = pd.DataFrame({
    "roll": range(1, 21),
    "name": ["A1","A2","A3","A4","A5","A6","A7","A8","A9","A10",
             "A11","A12","A13","A14","A15","A16","A17","A18","A19","A20"],
    "score": [88,92,76,81,67,95,72,84,91,56,78,89,69,94,73,82,90,77,85,80]
})


transactions = pd.DataFrame({
    "transaction_id": [101,102,103,104,105],
    "amount": [12000, 3500, 7500, 25000, 6000],
    "method": ["UPI", "CARD", "UPI", "CASH", "UPI"],
    "country": ["India", "USA", "India", "India", "UK"]
})

7. Changing Index to booking_id (Hotel Bookings)

bookings = pd.DataFrame({
    "booking_id": [1001,1002,1003,1004],
    "guest_name": ["Rahul", "Sneha", "Michael", "Sara"],
    "nights": [3,2,5,1],
    "room_type": ["Deluxe", "Standard", "Suite", "Standard"]
})


crypto = pd.DataFrame({
    "coin": ["BTC","ETH","SOL","XRP","DOGE","ADA"],
    "open": [54000, 3200, 185, 0.88, 0.10, 1.25],
    "high": [55000, 3400, 200, 0.92, 0.11, 1.35],
    "low":  [53000, 3100, 170, 0.85, 0.09, 1.15],
    "close":[54500, 3300, 190, 0.90, 0.105,1.30]
})


fitness = pd.DataFrame({
    "steps": [5000, 12000, 8000, 3000],
    "calories": [250, 420, 315, 150]
})


accounts = pd.DataFrame({
    "acc_no":[5001,5002,5003],
    "name":["Amit","Riya","John"],
    "balance":[90000, 78000, 65000]
})


cars = pd.DataFrame({
    "model":["Swift","Creta","City"],
    "engine":["1.2L","1.5L","1.5L"],
    "mileage":[21, 17, 18],
    "color":["Red","White","Black"]
})


users = pd.DataFrame({
    "user_id":[101,102,103,108,110],
    "name":["A","B","C","D","E"],
    "status":["active","inactive","active","inactive","active"]
})


ecom = pd.DataFrame({
    "order_id":[1,2,3,4,5],
    "product":["Shoes", None, "T-shirt", "Watch", None],
    "price":[1200, 800, None, 3500, 1500],
    "city":["Mumbai", None, "Delhi", "Pune", None]
})


finance = pd.DataFrame({
    "date":["2025-01-01","2025-01-02","2025-01-03"],
    "returns":["0.05","-0.02","0.01"],
    "volume":["1000","1500","900"]
})


16. Working with Strings (Customer Emails)

emails = pd.DataFrame({
    "email":["rahul@gmail.com", "meera@yahoo.com", "john@company.com"]
})
