Page 10 - IPP-12-2024

30. Consider the following dataframe: [3]
NAME CLASS MARKS

0 Abhay 9 78
1 Rubina 12 90
2 Ramandeep 10 34
3 Sonam 9 95
Write suitable Python statements for the following:
(i) Display names of all 9th class students.
(ii) Add a new column called grade with the data ['B','A','E','A']
(iii) Rename column NAME to SNAME.
Ans. (i) df[df.CLASS==9]['NAME'] or df.loc[df.CLASS==9]['NAME']
or df.loc[df.CLASS==9,'NAME']
(ii) df['grade']=['B','A','E','A']
(iii) df.rename(columns={'NAME':'SNAME'},inplace=True)

Section D
31. Consider the table STUDENT given below, and answer the questions that follow: [1+1+2]
RollNo Name Class Gender City Marks
1 Chetan XI M Mumbai 430
2 Geet XII F Agra 460
3 Preeti XI F Mumbai 470
4 Saniyal XII M Dubai 492
5 Manvi XII F Moscow 360
6 Nishant X M Dubai 256
(i) WAQ to display name in upper case.
(ii) WAQ to display marks of the topper student.
(iii) WAQ to count the total number of students of class XII.
Or (Option for part iii only)
WAQ to count the total number of students gender-wise.
Ans. (i) Select ucase(name) from student;
(ii) Select max(marks) as 'TopperMarks' from student;
(iii) Select count(*) from student where class = ‘XII’;
Or
Select gender, count(*) from student group by gender;
32. Mr. Narang, a data analyst, has designed the DataFrame df that contains data about sales containing year-
wise figures for five salespersons in INR with ‘Madhu’, ‘Kusum’, ‘Kinshuk’, ‘Ankit’ and ‘Shruti’ as indexes and
‘Y2014’, ‘Y2015’, ‘Y2016’ and ‘Y2017’ as columns shown below. Answer the questions that follow: [1+1+2]
Y2014 Y2015 Y2016 Y2017
Madhu 100.5 12000 20000 50000
Kusum 150.8 18000 50000 60000
Kinshuk 200.9 22000 70000 70000
Ankit 30000.0 30000 100000 80000
Shruti 40000.0 45000 125000 90000
(a) Predict the output of the following Python statement:
(i) df.size
(ii) df[2:4]
(b) Write a Python statement to display the sales made by Madhu and Ankit in the year Y2015 and Y2017
column.
Or
(Option for part b only)
Write a Python statement to compute and display the sum of sales of years Y2015 and Y2017 column of
the above given DataFrame.
A.32 Informatics Practices with Python–XII