Warmup

• Create a list with 2 items
• Create a tuple with 3 items
• Create a dictionary with 2 items

Agenda / Looking ahead

• This week we continue and conclude with Unit 3
• Topics: list comprehensions, 2D lists, and string operations
• Unit 3 Quiz 2 on Thursday
• Unit 3 Exam on Friday
• Begin Unit 4 (functions and exceptions) on Friday

Due Dates

• Assignment 3.6 due today

Lesson 3.7 — List Comprehensions: Map and Filter

• Today’s goal: write clear, concise list comprehensions for mapping and filtering.
• Why they matter: compact, expressive data transformations on lists.
• You will convert loops to comprehensions and decide when not to use them.

Warm-Up: Convert This Loop

nums = [1, 2, 3, 4, 5]
result = []
for n in nums:
    result.append(n * n)
print(result)

• Task: rewrite using a list comprehension.
• Question: when is the loop clearer than the comprehension?

Syntax Basics

• Pattern: [expression for item in iterable]
• Example: squares of 1..5

squares = [n * n for n in range(1, 6)]
print(squares)  # [1, 4, 9, 16, 25]

• Read left-to-right as: for each n, compute n*n and collect into a list.

Quick Practice

• Build a list of cubes from 0..9.
• Predict: what is len([c for c in 'python'])?
• Code it, run it, verify your prediction.

Filtering With if

• Pattern: [expression for item in iterable if condition]
• Example: keep only positive numbers, then double them

data = [-3, -1, 0, 2, 4]
out = [x * 2 for x in data if x > 0]
print(out)  # [4, 8]

• The trailing if filters items before the expression is applied.

Practice: Odd Squares Under 50

• Build odd squares less than 50 from 1..20 using one comprehension.
• Hint: combine if with the square expression.
• Extension: what changes to generate even squares under 100?

Map + Filter Together

• We often normalize values while filtering.

names = ['  ALIce', 'bob  ', '   ', 'Eve']
clean = [n.strip().title() for n in names if n.strip()]
print(clean)  # ['Alice', 'Bob', 'Eve']

• Question: why call strip() twice? Can you avoid the second call?

Practice: Emails → Usernames

• Given emails = ['ada@school.edu', 'ben@gmail.com', 'cai@college.edu']
• Build a list of usernames for only .edu addresses: ['ada', 'cai']
• Hint: str.endswith, str.split('@', 1)

Conditional Expression in Expression Part

• Pattern: [expr_if_true if cond else expr_if_false for item in iterable]

grades = [92, 67, 81, 58]
labels = ['Pass' if g >= 70 else 'Fail' for g in grades]
print(labels)  # ['Pass', 'Fail', 'Pass', 'Fail']

• Note: if inside expression is different from trailing filter if.

Practice: Letter Grade Buckets

• Map numeric grades to letters in one comprehension: A/B/C/D/F.
• Hint: nest conditional expressions carefully, or prefer a loop for clarity.

Nested Loops in Comprehensions

• Pattern: [expr for a in A for b in B]

A = [1, 2]
B = ['x', 'y']
pairs = [(a, b) for a in A for b in B]
print(pairs)  # [(1, 'x'), (1, 'y'), (2, 'x'), (2, 'y')]

• Flatten one level of a 2D list:

matrix = [[1, 2], [3, 4, 5], [6]]
flat = [x for row in matrix for x in row]
print(flat)  # [1, 2, 3, 4, 5, 6]

Practice: Row Sums and Flatten > Threshold

• Given grid = [[2, -1, 3], [0, 5], [7, -2, 4]]:
• Build row_sums list via comprehension.
• Build pos_flat of all positive elements using a nested comprehension.

Copy vs. Alias; Shallow vs. Deep

• Copy a list: [x for x in lst] or lst.copy() (shallow copies only).
• Pitfall: nested lists still share inner objects.

rows = [[0]*3 for _ in range(3)]  # correct 2D init

• Question: why is [[0]*3]*3 problematic?

Pitfalls and When Not to Use

• Avoid side effects in comprehensions (e.g., print, append).
• Prefer explicit loops when logic is long or branching heavily.
• Watch readability: short, linear transforms only.

Exit Ticket — Quick Check

• What’s the difference between [...] if cond vs. [...] if cond else ...?
• Write a comprehension for even numbers 0..20 inclusive.
• Flatten [[1,2],[3],[4,5]] in one line.
• When would you avoid using a comprehension?