1)
<HTML><head><TITLE><CENTER>My City</CENTER></TITLE>
</head>
<body style="background-color:pink;">
<h1 style="text-align: center ; Color: blue;"> My City name: Pune </h1>
<h1 style="font-size=30; color: powderblue; "> ::Pune city Landmarks:: </h1>
<ul >
<li style="color : blue; font-size=20; font-family:verdana;">National Defence Academy</li>
<li style="color : red; font-size=21; font-family:arial;">Dagadusheth Halwai Ganapati Temple</li>
<li style="color : green ;font-size=19; font-family:courier;">ISKCON NVCC
28
Temple</li>
<li style="color : yellow; font-size=22; font-family:Calibri;">Pune University</li>
</ul>
<div style="background-color: lightblue;width: 210px; height: 210px; overflow: scroll;">
Pune, also called ‘Poona’, is situated in the state of Maharashtra, India.
It is also known as the ‘Queen of the Deccan’ and attained its high importance when it became the capital
of Bhonsle Marathas in the 17th century.
</div>
<img style="border: 1px solid red; border-radius: 4px; padding: 5px; width: 150px;" src="puneimage.jpg"></img>
</body>
</HTML>
------------------
2) A
# importing pandas module
import pandas as pd
# loading data set
data = pd.read_csv('item.csv')
# display the data
print(data)
# replacing missing values in quantity
# column with mean of that column
data['quantity'] = data['quantity'].fillna(data['quantity'].mean())
# replacing missing values in price column
# with median of that column
data['salary'] = data['salary'].fillna(data['salary'].median())
# replacing missing values in bought column with
# standard deviation of that column
data['age'] = data['age'].fillna(data['age'].std())
print(Data)
-----------------
2)B
import matplotlib.pyplot as plt
import numpy as np
plt.title("Line Graph : Name vs Salary")
x = np.array(["Ajay", "Suresh", "Monica", "Rajesh"])
plt.xlabel("Employee Name")
y = np.array([10000, 25000, 15000, 5000])
plt.ylabel("Salary")
plt.plot(x, y)
plt.show()
-------------------------------
2. C)
import pandas as pd
pd.set_option('display.max_rows', 10)
pd.set_option('display.max_columns', 20)
diamonds = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/diamonds.csv')
print("First 10 rows:")
print(diamonds.head())
------------------
Q1)
<!DOCTYPE html>
<html>
<head>
<title>About my City</title>
<style type="text/css">
.blink{
font-family: arial;
font-weight: bold;
color: #ff5400;
animation: blink 0.3s infinite;
}
@keyframes blink{
0%{
visibility: hidden;
}
50%{
visibility: hidden;
}
100%{
visibility: visible;
}
}
</style>
</head>
<body bgcolor="pink">
<h1><b><font color="blue" size="20px"> SINNER </b></font></h1>
<h3><font face="impact" color="red">Near Old Bharat Gas Agency</font></h3>
<h3><font face="arial" color="violet">Near Renuka Jwellers</font></h3>
<h3><font face="serif" color="yellow">Mughalsarai Railway Station</font></h3>
<h1 class="blink" align="center">bhonsala military college nashik </h1>
<img src="login.png" style="height:200px;width:200px" >
</body>
</html>
Q2)A
import csv
import statistics
# Open the CSV file in read mode
with open('data.csv', 'r') as file:
# Create a CSV reader object
reader = csv.reader(file)
# Initialize variables to store the sum and count of the salary and age columns
salary_sum = 0
salary_count = 0
age_sum = 0
age_count = 0
# Iterate over the rows in the CSV file
for row in reader:
# Try to convert the salary and age values to floats
try:
salary = float(row[1])
age = float(row[2])
except ValueError:
# If the conversion fails, skip this row
continue
# Add the salary and age values to the sums
salary_sum += salary
age_sum += age
# Increment the count of salary and age values
salary_count += 1
age_count += 1
# Calculate the means of the salary and age columns
salary_mean = salary_sum / salary_count
age_mean = age_sum / age_count
# Open the CSV file in write mode
with open('data.csv', 'w', newline='') as file:
# Create a CSV writer object
writer = csv.writer(file)
# Open the CSV file in read mode again
with open('data.csv', 'r') as file:
# Create a CSV reader object
reader = csv.reader(file)
# Iterate over the rows in the CSV file
for row in reader:
# Try to convert the salary and age values to floats
try:
salary = float(row[1])
age = float(row[2])
except ValueError:
# If the conversion fails, set the values to the means
salary = salary_mean
age = age_mean
# Write the row to the CSV file with the updated salary and age values
writer.writerow([row[0], salary, age])
Q2)B
import matplotlib.pyplot as plt
# Data for the plot
names = ['Alice', 'Bob', 'Charlie', 'Dave', 'Eve']
salaries = [55000, 60000, 62000, 58000, 61000]
# Create the plot
plt.plot(names, salaries)
# Add title and axis labels
plt.title('Salary by Name')
plt.xlabel('Name')
plt.ylabel('Salary')
# Show the plot
plt.show()
Q2)C
import pandas as pd
# download the dataset from the internet
url = 'https://raw.githubusercontent.com/openai/heiweights/master/data/heiweights.csv'
df = pd.read_csv(url)
# print the first 10 rows of the dataframe
print(df.head(10))
# print the last 10 rows of the dataframe
print(df.tail(10))
# print 20 random rows of the dataframe
print(df.sample(20))
# print the shape of the dataframe
print(df.shape)
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