Coding Interview Prep · Lesson

Comprehensions and Built-ins

Write concise solutions using list/dict/set comprehensions, map, filter, zip, enumerate, and sorted with key functions.

Lesson 3 of 413 steps

Comprehensions and Built-ins is a free Coding Interview Prep lesson on CoddyKit. This is lesson 3 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Coding Interview Prep learning path, and your progress syncs across the web and the CoddyKit app. The Coding Interview Prep course includes 4 lessons in total.

List Comprehensions: Concise Filtering

A list comprehension turns a for-loop-plus-append into one clean line: [expr for item in iterable if condition]. It is a little faster and signals Python fluency.

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

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

# With filter
evens = [n for n in range(10) if n % 2 == 0]
print(evens)    # [0, 2, 4, 6, 8]

Nested Comprehensions for 2D Grids

Nested comprehensions build 2D grids — the standard way to set up a DP table. Avoid [[0]*C]*R, which shares one inner list across every row. The code shows the fix.

# WRONG: all rows are the same object!
bad = [[0] * 3] * 3
bad[0][0] = 9
print(bad)  # [[9,0,0],[9,0,0],[9,0,0]]  oops!

# CORRECT: each row is a separate list
good = [[0] * 3 for _ in range(3)]
good[0][0] = 9
print(good)  # [[9,0,0],[0,0,0],[0,0,0]]

Dict and Set Comprehensions

Dict and set comprehensions use braces: {k: v for ...} for a dict, {expr for ...} for a set. Both can filter, so you can transform or dedupe in a single line.

# Dict comprehension: square lookup
sq_map = {n: n**2 for n in range(1, 6)}
print(sq_map)  # {1:1, 2:4, 3:9, 4:16, 5:25}

# Set comprehension: unique lengths
words = ['cat', 'dog', 'elephant', 'ant']
unique_lengths = {len(w) for w in words}
print(unique_lengths)  # {3, 8}  (order varies)

Generator Expressions: Memory-Efficient

Wrap a comprehension in () and you get a generator that yields values one at a time, saving memory. Feed it straight into sum, max, or any over huge sequences.

# List comprehension builds all values at once
total = sum([n**2 for n in range(1_000_000)])

# Generator yields one at a time — lower memory
total = sum(n**2 for n in range(1_000_000))
print(total)  # 333332833333500000

# any/all with generators short-circuit early
nums = [4, 6, 8, 3, 10]
has_odd = any(n % 2 == 1 for n in nums)
print(has_odd)  # True  (stops at 3)

map() and filter(): Functional Style

map applies a function to every item; filter keeps the ones that pass a test. Both are lazy, so wrap in list() to see results. Comprehensions are often clearer.

nums = [1, 2, 3, 4, 5]

# map: apply function to each element
doubled = list(map(lambda n: n * 2, nums))
print(doubled)  # [2, 4, 6, 8, 10]

# filter: keep elements passing predicate
evens = list(filter(lambda n: n % 2 == 0, nums))
print(evens)    # [2, 4]

# Equivalent comprehensions (often preferred)
doubled = [n * 2 for n in nums]
evens   = [n for n in nums if n % 2 == 0]

zip(): Pairing Sequences

zip pairs two sequences and stops at the shorter one — the clean way to loop two lists at once. The trick zip(*matrix) transposes a 2D list. See the code.

keys   = ['a', 'b', 'c']
values = [1, 2, 3]

pairs = list(zip(keys, values))
print(pairs)  # [('a',1), ('b',2), ('c',3)]

# Build dict from two lists
d = dict(zip(keys, values))
print(d)      # {'a':1, 'b':2, 'c':3}

# Transpose a matrix
matrix = [[1,2,3],[4,5,6],[7,8,9]]
transposed = [list(row) for row in zip(*matrix)]
print(transposed)  # [[1,4,7],[2,5,8],[3,6,9]]

enumerate(): Index Plus Value

enumerate gives you (index, value) as you loop — cleaner than range(len(lst)) and free of off-by-one slips. Use the start option to begin counting at 1.

fruits = ['apple', 'banana', 'cherry']

# Instead of: for i in range(len(fruits)):
for i, fruit in enumerate(fruits):
    print(i, fruit)
# 0 apple / 1 banana / 2 cherry

# Start from 1
for i, fruit in enumerate(fruits, 1):
    print(f'{i}. {fruit}')
# 1. apple / 2. banana / 3. cherry

sorted() with Key Functions

sorted returns a new sorted list and takes a key function for custom order. Sort by length, by a tuple field, or case-insensitively. The code shows multi-key sorts.

# Sort by second element of tuple
intervals = [(1,3),(2,1),(0,5)]
print(sorted(intervals, key=lambda x: x[1]))
# [(2,1),(1,3),(0,5)]

# Sort strings case-insensitively
words = ['Banana', 'apple', 'Cherry']
print(sorted(words, key=str.lower))
# ['apple', 'Banana', 'Cherry']

# Sort by multiple keys: first by length, then alphabetically
words = ['fig', 'apple', 'ant', 'kiwi']
print(sorted(words, key=lambda w: (len(w), w)))
# ['ant', 'fig', 'kiwi', 'apple']

min() and max() with Key

min and max take a key too, so you can grab the element with the smallest or largest mapped value in one call — like the longest word. See the code.

words = ['banana', 'fig', 'strawberry', 'kiwi']

longest = max(words, key=len)
print(longest)   # strawberry

shortest = min(words, key=len)
print(shortest)  # fig

# Find interval with earliest end
intervals = [(2,6),(1,3),(4,5)]
earlist_end = min(intervals, key=lambda x: x[1])
print(earlist_end)  # (1, 3)

any() and all() for Short-Circuit Checks

any stops at the first truthy item; all stops at the first falsy one. Both short-circuit, so paired with a generator they test conditions fast and lazily.

nums = [2, 4, 6, 7, 8]

all_even = all(n % 2 == 0 for n in nums)
print(all_even)  # False  (7 is odd)

has_large = any(n > 5 for n in nums)
print(has_large) # True  (6 qualifies, stops there)

# Practical: check if sudoku row has no duplicates
row = [1, 2, 3, 4, 5, 6, 7, 8, 9]
valid = all(1 <= n <= 9 for n in row) and len(set(row)) == 9
print(valid)  # True

sum(), abs(), and divmod()

Three math helpers show up everywhere: sum, abs, and divmod. divmod(a, b) returns both the quotient and remainder at once — perfect for pulling digits.

# sum with generator
print(sum(n**2 for n in range(1, 6)))  # 55

# abs for distance problems
print(abs(-7))   # 7

# divmod for digit extraction
num = 1234
digits = []
while num:
    num, d = divmod(num, 10)
    digits.append(d)
digits.reverse()
print(digits)  # [1, 2, 3, 4]

Quick Check

Quick check — let us see how the comprehensions and built-ins landed. One question, you have got this. ✅

Lesson Recap

Recap: comprehensions turn loops into one-liners, built-ins like zip and sorted take key functions, and generators save memory for single-pass work.

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Is the “Comprehensions and Built-ins” lesson free?

Yes — the full text of “Comprehensions and Built-ins” is free to read here on the web. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Coding Interview Prep course, upgrade to CoddyKit PRO. The Coding Interview Prep course includes 4 lessons in total.

What will I learn in “Comprehensions and Built-ins”?

Write concise solutions using list/dict/set comprehensions, map, filter, zip, enumerate, and sorted with key functions. You practise Coding Interview Prep with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start Coding Interview Prep?

No prior experience is required. Coding Interview Prep on CoddyKit is structured for beginners through advanced learners, so you can start here or from the beginning and move at your own pace. This is lesson 3 of 4.

How long does the “Comprehensions and Built-ins” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

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Yes. Every Coding Interview Prep lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.

All lessons in this course

  1. Lists, Tuples, and Slicing
  2. Dictionaries and Sets in Python
  3. Comprehensions and Built-ins
  4. Functions, Closures, and Lambda
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