Q1]

<!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>About My City</title> <style> body { background-color: pink; /* Pink background color */ margin: 0; padding: 0; }

#cityName {
  font-size: 24pt; /* Large text size */
  color: blue; /* Blue text color */
  text-align: center;
  margin-top: 20px;
}

.landmark {
  margin: 10px 0;
}

#landmark1 {
  color: green; /* Different color */
  font-style: italic; /* Different style */
  font-family: 'Arial', sans-serif; /* Different font */
}

#landmark2 {
  color: purple; /* Different color */
  font-weight: bold; /* Different style */
  font-family: 'Verdana', sans-serif; /* Different font */
}

#landmark3 {
  color: orange; /* Different color */
  text-decoration: underline; /* Different style */
  font-family: 'Georgia', serif; /* Different font */
}

#image {
  display: block;
  margin: 20px auto;
}

</style> </head> <body> <h1 id="cityName">My City</h1> <div class="landmark" id="landmark1">Landmark 1</div> <div class="landmark" id="landmark2">Landmark 2</div> <div class="landmark" id="landmark3">Landmark 3</div> <img src="your_image_url.jpg" alt="City Image" id="image" style="width: 80%;"> </body> </html> Q2] a] import pandas as pd

Load data from Data.csv

data = pd.read_csv('Data.csv')

Replace missing values with mean

data['salary'].fillna(data['salary'].mean(), inplace=True)
data['age'].fillna(data['age'].mean(), inplace=True)

Save the modified data to a new CSV file if needed

data.to_csv('Data_filled.csv', index=False)

B]
import pandas as pd
import matplotlib.pyplot as plt

Assuming 'Data.csv' has a 'name' and 'salary' column

data = pd.read_csv('Data.csv')

Generate a line plot

plt.plot(data['name'], data['salary'])
plt.xlabel('Name')
plt.ylabel('Salary')
plt.title('Name vs Salary')
plt.xticks(rotation=90) # Rotate x-axis labels for better visibility
plt.show()

c]
import pandas as pd

Assuming the dataset is named 'heights_weights.csv'

data = pd.read_csv('heights_weights.csv')

Display the first 10 rows

print("First 10 rows:")
print(data.head(10))

Display the last 10 rows

print("\nLast 10 rows:")
print(data.tail(10))

Display random 20 rows

print("\nRandom 20 rows:")
print(data.sample(20))

Display the shape of the dataset

print("\nShape of the dataset:")
print(data.shape)

Hslip2