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CBSE · Class 12 · Informatics Practices

Data Handling using Pandas – II

Standard difficulty · 20 questions · 40 marks · Time: 39 minutes

Name: Date: Score: ____ / 40

Show your working for Sections B and C. Answers are on the last page — try every question first!

Section A — Choose the correct answer8 × 1 = 8 marks

1.

Which function gives the middle value of a sorted column?

mode()
count()
mean()
median()

2.

DataFrame df:
index | Item | Store | Price | Stock
P1 | Folder | West | 24 | 38
P2 | Scissors | Central | 69 | 62
P3 | Crayons | North | 13 | 44
P4 | Sharpener | Central | 74 | 56
P5 | Glue | Central | 52 | 57
What does print(df['Stock'].median()) show?

62.0
38.0
56.0
51.4

3.

DataFrame df:
index | Name | Dept | Q1 | Q2
E1 | Tara | Support | 84 | 73
E2 | Gita | Design | 82 | 92
E3 | Vikram | Design | 70 | 81
E4 | Asha | Design | 87 | 85
E5 | Rohan | Accounts | 53 | 86
What does print(df['Q1'].mean()) show?

82.0
87.0
376
75.2

4.

DataFrame df:
index | Name | Dept | Q1 | Q2
E1 | Tara | Support | 45 | 78
E2 | Manav | Training | 57 | 86
E3 | Arjun | Support | 54 | 82
E4 | Kabir | Training | 21 | 66
E5 | Isha | Design | 24 | 45
E6 | Sana | Design | 39 | 53
In print(df.sort_values(by='Q2')), which Name is in row 3 of the output?

Arjun
Sana
Isha
Kabir

5.

DataFrame df:
index | Name | City | Maths | Science
S1 | Isha | Delhi | 46 | 45
S2 | Zoya | Shimla | 35 | 60
S3 | Pooja | Delhi | 63 | 50
S4 | Farhan | Shimla | 72 | 42
S5 | Imran | Indore | 69 | 74
S6 | Neha | Delhi | 94 | 37
S7 | Karan | Shimla | 88 | 83
What does print(df.groupby('City')['Science'].min()['Shimla']) show?

74
43
42
37

6.

DataFrame df:
index | Item | Store | Price | Stock
P1 | Ruler | West | 60 | 56
P2 | Glue | South | 13 | 57
P3 | Folder | West | — | 15
P4 | Eraser | South | 61 | 88
P5 | Stapler | North | 21 | 77
P6 | Pen | North | 18 | 72
(— marks a missing value; pandas prints it as Not a Number.)
What does print(df['Price'].count()) show?

5
4
6
1

7.

DataFrame df:
index | Name | Dept | Q1 | Q2
E1 | Gita | Accounts | 24 | 26
E2 | Imran | Accounts | — | 77
E3 | Tara | Design | 42 | 44
E4 | Zoya | Support | 25 | 81
E5 | Neha | Support | — | 22
(— marks a missing value; pandas prints it as Not a Number.)
What does print(df['Q1'].isnull().sum()) show?

0
5
2
3

8.

DataFrame df:
index | Name | Dept | Q1 | Q2
E1 | Dev | Accounts | 86 | 55
E2 | Karan | Design | 81 | 32
E3 | Meena | Design | — | 47
E4 | Diya | Accounts | — | 49
E5 | Vikram | Accounts | 87 | 20
E6 | Farhan | Support | 91 | 31
(— marks a missing value; pandas prints it as Not a Number.)
What does print(df.dropna().shape) show?

(4, 3)
(6, 4)
(3, 4)
(4, 4)

Section B — Short answer8 × 2 = 16 marks

9.

What is descriptive statistics? Name four functions pandas provides for it.

10.

DataFrame df:
index | Item | Store | Price | Stock
P1 | Sharpener | Central | 24 | 35
P2 | Folder | Central | 67 | 25
P3 | Scissors | East | 33 | 76
P4 | Ruler | East | 25 | 52
P5 | Notebook | South | 19 | 47
P6 | Stapler | South | 57 | 20
Write the output of:
print(df['Stock'].max() - df['Stock'].min())

11.

DataFrame df:
index | Name | City | Maths | Science
S1 | Pooja | Indore | 79 | 39
S2 | Farhan | Pune | 75 | 86
S3 | Meena | Indore | 84 | 55
S4 | Asha | Mysuru | 64 | 73
S5 | Karan | Indore | 73 | 48
S6 | Manav | Indore | 98 | 96
Which values does print(df.sort_values(by='Science').head(2)['Name']) show? Write them in order, separated by commas.

12.

DataFrame df:
index | Item | Store | Price | Stock
P1 | Sharpener | South | 81 | 27
P2 | Stapler | North | 55 | 90
P3 | Crayons | West | 37 | 16
P4 | Diary | South | 83 | 28
P5 | Marker | North | 19 | 78
P6 | Pen | West | 26 | 57
P7 | Glue | North | 32 | 50
Write the output of:
print(df.groupby('Store')['Price'].count()['South'])

13.

DataFrame df:
index | Name | Dept | Q1 | Q2
E1 | Diya | Training | — | 73
E2 | Farhan | Accounts | — | 53
E3 | Arjun | Accounts | 55 | 60
E4 | Karan | Design | 89 | 37
E5 | Dev | Accounts | 81 | 95
(— marks a missing value; pandas prints it as Not a Number.)
Write the output of:
print(df['Q1'].fillna(0).sum())

14.

DataFrame df:
index | Name | Dept | Q1 | Q2
E1 | Zoya | Support | 66 | 45
E2 | Sana | Training | 49 | 67
E3 | Ravi | Sales | — | 65
E4 | Asha | Support | — | 78
E5 | Arjun | Training | 63 | 90
(— marks a missing value; pandas prints it as Not a Number.)
Write the output of:
print(df['Q1'].count())

15.

DataFrame df:
index | Item | Store | Price | Stock
P1 | Crayons | Central | 29 | 85
P2 | Eraser | Central | 24 | 17
P3 | Glue | North | 14 | 62
P4 | Pen | North | 80 | 60
P5 | Marker | West | 39 | 80
P6 | Diary | Central | 51 | 78
Write the output of:
print(df.groupby('Store')['Price'].sum())
(Write one "label | value" line for each group.)

16.

DataFrame df:
index | Item | Store | Price | Stock
P1 | Crayons | Central | — | 66
P2 | Marker | North | 36 | 57
P3 | Scissors | North | 13 | 80
P4 | Stapler | South | 89 | 33
P5 | Folder | South | 73 | 78
(— marks a missing value; pandas prints it as Not a Number.)
Write the output of:
print(df['Price'].mean())

Section C — Word problems4 × 4 = 16 marks

17.

Explain any four statistical functions of pandas with examples on a column Marks.

18.

DataFrame df:
index | Item | Store | Price | Stock
P1 | Eraser | South | — | 90
P2 | Diary | North | 74 | 50
P3 | Pen | East | 25 | 27
P4 | Notebook | East | 21 | 56
P5 | Folder | East | 60 | 72
P6 | Crayons | North | 45 | 75
(— marks a missing value; pandas prints it as Not a Number.)
Write the output of:
(a) print(df['Price'].count())
(b) print(df['Price'].sum())
(c) print(df.dropna().shape)

19.

DataFrame df:
index | Name | Dept | Q1 | Q2
E1 | Isha | Sales | 61 | 79
E2 | Gita | Accounts | 83 | 48
E3 | Rohan | Support | 71 | 64
E4 | Meena | Support | 60 | 93
E5 | Zoya | Accounts | 42 | 72
E6 | Diya | Sales | 45 | 68
Write the output of:
(a) print(df.groupby('Dept')['Q1'].sum())
(b) print(df.groupby('Dept')['Q1'].max())

20.

DataFrame df:
index | Item | Store | Price | Stock
P1 | Folder | North | 41 | 46
P2 | Sharpener | East | 54 | 49
P3 | Eraser | East | 30 | 85
P4 | Crayons | North | 83 | 70
P5 | Stapler | South | 27 | 63
Write the output of:
(a) print(df['Price'].max())
(b) print(df['Price'].min())
(c) print(df['Price'].mean())
(d) print(df['Price'].median())

AmbifulWorksheets

Practice that never repeats
CBSE · Class 12 · Informatics Practices

Answer key

Data Handling using Pandas – II · AW-12IP-PD2-2026-V1 · For parents — fold back or tear off before handing the sheet to your child.

Q	Answer	Working
1	(d) median()	—
2	(c) 56.0	sorted: 38, 44, 56, 57, 62; the middle value
3	(d) 75.2	376 ÷ 5; mean() always gives a float
4	(d) Kabir	ascending order of Q2: Isha, Sana, Kabir, Tara, Arjun, Manav
5	(c) 42	the rows with City Shimla
6	(a) 5	count() leaves out missing values
7	(c) 2	isnull() is True for each missing value; sum() counts the Trues
8	(d) (4, 4)	dropna() drops every row that has a missing value
9	Summarising data by numbers that describe it, such as max(), min(), mean(), median(), mode(), count(), sum(), std(), var() and quantile().	—
10	56	76 − 20
11	Pooja, Karan	sorted by Science, lowest first; the first 2 rows
12	2	count() over the rows with Store South
13	225.0	missing values become 0; the column holds floats, so the sum is a float
14	3	missing values are skipped
15	Central \| 104 North \| 94 West \| 39	one value per group; groupby sorts the group labels
16	52.75	missing values are skipped: 211 ÷ 4
17	df['Marks'].max() gives the highest mark; min() the lowest; mean() the average (sum ÷ count); median() the middle value of the sorted marks; mode() the most frequent; std() the spread around the mean; count() the number of values present.	one mark for each function
18	(a) 5; (b) 225.0; (c) (5, 4)	(a) and (b) skip missing values, and the column is a float column; (c) drops the rows with a missing value
19	(a) Accounts \| 125 Sales \| 106 Support \| 131 (b) Accounts \| 83 Sales \| 61 Support \| 71	one line per group, the group labels in sorted order
20	(a) 83; (b) 27; (c) 47.0; (d) 41.0	sorted: 27, 30, 41, 54, 83; total 235 over 5 values