2) C

3) C

4) A

5) C

6) D

7) C

8) D

9) C

10) D

2) C

3) D

4) A

5) C

6) C

7) D

8) B

9) C

10) C

2 - C

3 - C

4 - D incorrect - A is correct

5 - A incorrect - C is correct

6 - D

7 - C

8 - A incorrect - B is correct

9 - C

10 - D

df2.to_csv('Priceunder50.csv', index = False)

Electronics

2. filter Employess marked as 'Late'

3. Filter Employess from It Department

4. Filter employes Absent on a Specific date

2: Filter Failed Login Attempts

3: Find Login Attempts from a Specific IP Address

4: Filter Logins in a Time Range

employee = pd.read_csv('employee_attendance.csv')
employee

2)

late_employees = employee[employee['Status'] == 'Late']
late_employees

3)

it_employees = employee[employee['Department'] == 'IT']
it_employees

4)

absent_employees = employee[(employee['Status'] == 'Absent') & (employee['Date'] == '2024-02-02')]
absent_employees

df1=pd.read_csv('employee_attendance.csv')

df1

df1_late = df1[df1['Status'] == 'Late']

df1_late

df1_it = df1[df1['Department'] == 'IT']

df1_it

df1_absent = df1[(df1['Status'] 'Absent') & (df1['Date'] '2024-02-02')]

df1_absent

df2 = pd.read_csv('cybersecurity_logins.csv')
df2

2)

failed_logins = df2[df2['Success'] == 'No']
failed_logins

3)

specific_ip_attempts = df2[df2['IP_Address'] == '192.168.1.10']
specific_ip_attempts

4)

time_range_attempts = df2[(df2['Timestamp'] >= '2024-02-01 08:30:00') & (df2['Timestamp'] <= '2024-02-01 09:10:00')]
time_range_attempts

1

df2 = pd.read_csv('cybersecurity_logins.csv')

df2.info()

2

df2_unsuccessful = df2[df2['Success'] == 'No']

df2_unsuccessful

3

df2_ip_filtered = df2[df2['IP_Address'] == '192.168.1.10']

df2_ip_filtered

4

4

df2_time_filtered = df2[

(df2['Timestamp'] >= '2024-02-01 08:30:00') &

(df2['Timestamp'] <= '2024-02-01 12:00:00')]

df2_time_filtered

failed_logins = df3[df3['Success'] == 'No']
failed_logins

ip_address = '192.168.1.1'
login_attempts_from_ip = df3[df3['IP_Address'] == ip_address]
login_attempts_from_ip

time_range_attempts = df3[(df3['Timestamp'] >= '2024-02-01') & (df3['Timestamp'] <= '2024-02-05')]
time_range_attempts

mean

median

mode

The range

The Mean

Interpret the value of r

df['Revenue'].min()

df['Revenue'].max()

df['Revenue'].sum()

#print(f"Total Price is {df['Price'].sum()}")

#print(f"Minimum revenue is {df['Price'].min()}")

#print(f"Maximum revenue is {df['Price'].max()}")

print(f"Total price is {df['Price'].sum()} per year")

print(f"Total number of transactions is {df['Price'].count()} per year")

print(f"Min price is {df['Price'].min()}")

print(f"Max price is {df['Price'].max()}")

print(f"The Median of price is {df['Price'].median()}")

print(f"The most common price is {df['Price'].mode()}")

print(f"Median Price: {df['Price'].median()}")

print(f"Mode of Price: {df['Price'].mode()[0]}")

print(f"Mean Revenue: {df['Revenue'].mean()}")

Standard Deviation

print(f"Standard Deviation of Revenue: {df['Revenue'].std()}")

Range

Revenue_range = df['Revenue'].max() - df['Revenue'].min()

print(f"Range of Revenue: {revenue_range}")

print(f"Range of Revenue: {df['Revenue'].max()-df['Revenue'].min()}")

print(f"Mean Revenue: {df['Revenue'].mean()}")

r2

print(f'Pearson correlation Coefficient: {r2}')

r

print(f'Pearson correlation coefficient: {r}')

2. C

3. C

4. D

5. C

6. D

7. C

8. B

9. D

10. B

2) C

3) C

4) B

5) C

6) C

7) C

8) B

9) D

10) C