Lists, Tuples & Sets — Exercises

Work with sequence operations, comprehensions, uniqueness and immutability.

1. Lists

Given nums = [1,2,3,4,5] create a new list with squares using a list comprehension.
Remove duplicates from [1,2,2,3,3,3] while preserving order.

2. Tuples

Create a tuple representing a point (x, y) and unpack into x and y.
Convert a tuple to a list, modify it, then convert back to a tuple.

3. Sets

Use sets to find common elements (intersection) of two lists.
Given a sentence string, count unique words using sets (case-insensitive).

4. Mixed challenge

Given list of tuples representing (name, score), produce a dict mapping name → highest score (use loops or comprehensions).
Given a large list of numbers, show how sets can speed up membership tests compared to lists (explain with simple timing code example).

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Show answers — Collections worksheet

1. Lists

# 1. squares using comprehension
nums = [1,2,3,4,5]
squares = [x*x for x in nums]

# 2. remove duplicates while preserving order
arr = [1,2,2,3,3,3]
seen = set()
result = []
for x in arr:
    if x not in seen:
        seen.add(x)
        result.append(x)
print(result)  # [1,2,3]

2. Tuples

# 3. unpack point
point = (3,4)
x, y = point

# 4. convert tuple to list, modify, convert back
t = (1,2,3)
lst = list(t)
lst[0] = 99
t2 = tuple(lst)

3. Sets

# 5. intersection of lists
a = [1,2,3]
b = [2,3,4]
common = list(set(a) & set(b))

# 6. unique words (case-insensitive)
s = "Hello hello world"
words = {w.lower() for w in s.split()}
print(words)

4. Mixed challenge

# 7. map name -> highest score
data = [("A", 10), ("B", 20), ("A", 15)]
best = {}
for name, score in data:
    best[name] = max(best.get(name, 0), score)
print(best)

# 8. timing membership difference (simple demonstration)
import time
large = list(range(1000000))
t0 = time.time()
999999 in large
t1 = time.time()
s = set(large)
t2 = time.time()
999999 in s
t3 = time.time()
print("list check:", t1-t0, "set check:", t3-t2)