1. Creating a DataFrame from Travel Package Data

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


import pandas as pd

df = pd.DataFrame({
    "destination": destination,
    "price": price,
    "duration_days": duration_days
})

print(df.head(2))


2. DataFrame from List of Lists (Stock Prices)

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


df = pd.DataFrame(data, columns=["stock", "open", "close"])
df["change"] = df["close"] - df["open"]
print(df)


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


print(df.shape)
print(df.columns)
print(df.dtypes)
print(df.size)
print(df.index)


Use double brackets to select multiple columns.

students.head(5)

students[(students["roll"] >= 10) & (students["roll"] <= 20)]


transactions[transactions["amount"] > 10000]

transactions[transactions["method"] == "UPI"]

transactions[(transactions["amount"] > 5000) & (transactions["country"] == "India")]


bookings_indexed = bookings.set_index("booking_id")
print(bookings_indexed)

reset_df = bookings_indexed.reset_index()
print(reset_df)


sales = pd.DataFrame({
    "city":["Delhi","Delhi","Mumbai","Mumbai"],
    "year":[2022,2023,2022,2023],
    "revenue":[12.5,14.2,10.8,11.9]
}).set_index(["city","year"])


sales.loc["Delhi"]

sales.loc[("Mumbai", 2022):("Mumbai", 2023)]

sales.loc["Delhi", "revenue"]


crypto.iloc[:3]           # first 3 rows
crypto.iloc[:, -2:]       # last 2 columns
crypto.iloc[2:6, 1:4]     # rows 2–5, columns 1–3


fitness["steps_km"] = fitness["steps"] * 0.0008
fitness["status"] = fitness["calories"].apply(lambda x: "Active" if x > 300 else "Low")


new_row = pd.DataFrame({"acc_no":[5004], "name":["Ritika"], "balance":[85000]})
accounts = pd.concat([accounts, new_row], ignore_index=True)


cars = cars.drop(columns=["color"])
del cars["engine"]


users = users[~users["user_id"].isin([102,108,110])]


ecom = ecom.dropna(subset=["product"])

ecom["price"] = ecom["price"].fillna(ecom["price"].median())

ecom["city"] = ecom["city"].fillna("Unknown")


finance["date"] = pd.to_datetime(finance["date"])
finance["returns"] = finance["returns"].astype(float)
finance["volume"] = finance["volume"].astype(int)


emails["username"] = emails["email"].str.split("@").str[0]
emails["domain"] = emails["email"].str.split("@").str[1]
emails["is_gmail"] = emails["domain"] == "gmail.com"


17. Datetime Operations (Flight Schedule)

flights["departure_time"] = pd.to_datetime(flights["departure_time"])
flights["arrival_time"] = pd.to_datetime(flights["arrival_time"])

flights["dep_hour"] = flights["departure_time"].dt.hour

flights["duration"] = flights["arrival_time"] - flights["departure_time"]

morning_flights = flights[
    flights["departure_time"].dt.hour.between(6, 12)
]


ohlc.columns = ohlc.columns.str.lower().str.replace(" ", "_")


merged = pd.merge(customers, orders, on="customer_id")
result = merged[["name", "amount"]]
