A filter is a condition that selects the records relevant to a question. A filtered iteration still visits the records systematically, but only matching records update the chosen answer.
Selected course cards show AND: keep a card only when both its city and gender pass the filter.
YOUR COURSE DATA · TRY IT HERE
Find female students from Chennai
Use the supplied 30 score cards. No download or coding is needed.
READY TO START
A pile of 30 cards
Start with the first card. Read its fields, do one small action, then move on.
▱ ▱ ▱
What we remember
Read
0
Nothing has been read yet. Choose Next card to begin.
Look through all 30 original records
BhuvaneshMaths 68 · Erode
CardNo
0
Name
Bhuvanesh
Gender
M
DateOfBirth
7 Nov
CityTown
Erode
Mathematics
68
Physics
64
Chemistry
78
Total
210
HarishMaths 62 · Salem
CardNo
1
Name
Harish
Gender
M
DateOfBirth
3 Jun
CityTown
Salem
Mathematics
62
Physics
45
Chemistry
91
Total
198
ShashankMaths 57 · Chennai
CardNo
2
Name
Shashank
Gender
M
DateOfBirth
4 Jan
CityTown
Chennai
Mathematics
57
Physics
54
Chemistry
77
Total
188
RidaMaths 42 · Chennai
CardNo
3
Name
Rida
Gender
F
DateOfBirth
5 May
CityTown
Chennai
Mathematics
42
Physics
53
Chemistry
78
Total
173
RitikaMaths 87 · Madurai
CardNo
4
Name
Ritika
Gender
F
DateOfBirth
17 Nov
CityTown
Madurai
Mathematics
87
Physics
64
Chemistry
89
Total
240
AkshayaMaths 71 · Chennai
CardNo
5
Name
Akshaya
Gender
F
DateOfBirth
8 Feb
CityTown
Chennai
Mathematics
71
Physics
92
Chemistry
84
Total
247
SameerMaths 81 · Ambur
CardNo
6
Name
Sameer
Gender
M
DateOfBirth
23 Mar
CityTown
Ambur
Mathematics
81
Physics
82
Chemistry
87
Total
250
AdityaMaths 84 · Vellore
CardNo
7
Name
Aditya
Gender
M
DateOfBirth
15 Mar
CityTown
Vellore
Mathematics
84
Physics
92
Chemistry
76
Total
252
SuryaMaths 74 · Bengaluru
CardNo
8
Name
Surya
Gender
M
DateOfBirth
28 Feb
CityTown
Bengaluru
Mathematics
74
Physics
64
Chemistry
51
Total
189
ClarenceMaths 63 · Bengaluru
CardNo
9
Name
Clarence
Gender
M
DateOfBirth
6 Dec
CityTown
Bengaluru
Mathematics
63
Physics
88
Chemistry
73
Total
224
KavyaMaths 64 · Chennai
CardNo
10
Name
Kavya
Gender
F
DateOfBirth
12 Jan
CityTown
Chennai
Mathematics
64
Physics
72
Chemistry
68
Total
204
RahulMaths 97 · Bengaluru
CardNo
11
Name
Rahul
Gender
M
DateOfBirth
30 Apr
CityTown
Bengaluru
Mathematics
97
Physics
92
Chemistry
92
Total
281
SrinidhiMaths 52 · Chennai
CardNo
12
Name
Srinidhi
Gender
F
DateOfBirth
14 Jan
CityTown
Chennai
Mathematics
52
Physics
64
Chemistry
71
Total
187
GopiMaths 65 · Madurai
CardNo
13
Name
Gopi
Gender
M
DateOfBirth
6 May
CityTown
Madurai
Mathematics
65
Physics
73
Chemistry
89
Total
227
SophiaMaths 89 · Trichy
CardNo
14
Name
Sophia
Gender
F
DateOfBirth
23 July
CityTown
Trichy
Mathematics
89
Physics
62
Chemistry
93
Total
244
GoutamiMaths 76 · Theni
CardNo
15
Name
Goutami
Gender
F
DateOfBirth
22 Sep
CityTown
Theni
Mathematics
76
Physics
58
Chemistry
90
Total
224
TauseefMaths 87 · Trichy
CardNo
16
Name
Tauseef
Gender
M
DateOfBirth
30 Dec
CityTown
Trichy
Mathematics
87
Physics
86
Chemistry
43
Total
216
ArshadMaths 62 · Chennai
CardNo
17
Name
Arshad
Gender
M
DateOfBirth
14 Dec
CityTown
Chennai
Mathematics
62
Physics
81
Chemistry
67
Total
210
AbiramiMaths 72 · Erode
CardNo
18
Name
Abirami
Gender
F
DateOfBirth
9 Oct
CityTown
Erode
Mathematics
72
Physics
92
Chemistry
97
Total
261
VetrivelMaths 56 · Trichy
CardNo
19
Name
Vetrivel
Gender
M
DateOfBirth
30 Aug
CityTown
Trichy
Mathematics
56
Physics
78
Chemistry
62
Total
196
KalyanMaths 93 · Vellore
CardNo
20
Name
Kalyan
Gender
M
DateOfBirth
17 Sep
CityTown
Vellore
Mathematics
93
Physics
68
Chemistry
91
Total
252
MonikaMaths 78 · Bengaluru
CardNo
21
Name
Monika
Gender
F
DateOfBirth
15 Mar
CityTown
Bengaluru
Mathematics
78
Physics
69
Chemistry
74
Total
221
PriyaMaths 62 · Nagercoil
CardNo
22
Name
Priya
Gender
F
DateOfBirth
17 Jul
CityTown
Nagercoil
Mathematics
62
Physics
62
Chemistry
57
Total
181
DeepikaMaths 97 · Bengaluru
CardNo
23
Name
Deepika
Gender
F
DateOfBirth
13 May
CityTown
Bengaluru
Mathematics
97
Physics
91
Chemistry
88
Total
276
SiddharthMaths 44 · Madurai
CardNo
24
Name
Siddharth
Gender
M
DateOfBirth
26 Dec
CityTown
Madurai
Mathematics
44
Physics
72
Chemistry
58
Total
174
GeetaMaths 87 · Chennai
CardNo
25
Name
Geeta
Gender
F
DateOfBirth
16 May
CityTown
Chennai
Mathematics
87
Physics
75
Chemistry
92
Total
254
JKMaths 74 · Chennai
CardNo
26
Name
JK
Gender
M
DateOfBirth
22 Jul
CityTown
Chennai
Mathematics
74
Physics
71
Chemistry
82
Total
227
JaganMaths 81 · Madurai
CardNo
27
Name
Jagan
Gender
M
DateOfBirth
4 Mar
CityTown
Madurai
Mathematics
81
Physics
76
Chemistry
52
Total
209
NishaMaths 74 · Madurai
CardNo
28
Name
Nisha
Gender
F
DateOfBirth
10 Sep
CityTown
Madurai
Mathematics
74
Physics
83
Chemistry
83
Total
240
NaveenMaths 72 · Vellore
CardNo
29
Name
Naveen
Gender
M
DateOfBirth
13 Oct
CityTown
Vellore
Mathematics
72
Physics
66
Chemistry
81
Total
219
VerbCount increases for run and jump, while every word card still becomes visited.
You can first separate matching cards into a pile and then count them. Or you can check each card and count a match immediately, in one scan. Both methods give the same selected set if the condition is unchanged.
VerbCount ← 0
For each word card X:
If X.category = "Verb":
VerbCount ← VerbCount + 1
Mark X as visited
Report VerbCount
The answer is 2 verbs among 7 visited word cards. The lecture’s own paragraph has reported counts of 7 verbs and 7 pronouns; do not confuse its totals with this small teaching dataset.
Initialize VerbCount and PronounCount to zero. Update the appropriate count from each card’s recorded category. If you also count Other, the three totals should add to the number of visited cards, assuming each card has exactly one category label.
An all-food bill, a no-food bill and a mixed bill affect the counters differently.
Count bills, not individual food items. For a nonempty bill, “all food” means every item is food. “No food” means none of its items are food. A mixed bill meets neither condition. “Not all food” includes both mixed and no-food bills, so it is not the same as “no food”.
Assume the tutorial bills contain at least one item. If empty bills are possible in another task, explicitly define their handling before using all/none conditions; excluding or flagging an empty bill avoids ambiguity in this exercise.
Try it yourself
A bill contains rice, milk and soap. Does it increase AllFoodCount or NoFoodCount?
Show explained answer
Neither. It has food and non-food items, so it is mixed.
Try it yourself
A bill contains four food item rows. How much does AllFoodCount increase?
Show explained answer
By 1, because the variable counts qualifying bills, not item rows.
Try it yourself
Five cards are visited and only two satisfy the filter. What are total visited and filtered count?
Show explained answer
Total visited = 5; filtered count = 2. A rejected card was still inspected.
Try it yourself
Why is “If not all food, add to NoFoodCount” wrong?
Show explained answer
A mixed bill is not all food but still contains some food. It should not increase NoFoodCount.
Source: Lecture 1.2; Tutorial 1.1. Small teaching datasets and diagrams are prepared for these notes; lecture and tutorial totals are identified explicitly.