Comprehensions and Unpacking

Python can build a collection from a rule in one readable line. A list comprehension reads like the sentence "for each item, keep/transform it":

nums = [1, 2, 3, 4, 5]
squares = [n * n for n in nums]          # [1, 4, 9, 16, 25]
evens    = [n for n in nums if n % 2 == 0]  # filter with a trailing if
labels   = [f"id-{n}" for n in nums]     # transform each element

Dict and set comprehensions follow the same shape:

words = ["tea", "coffee", "cola"]
lengths = {w: len(w) for w in words}     # dict: word -> length
unique  = {w[0] for w in words}          # set of first letters

Rule of thumb: if the comprehension needs more than one if or nested loops that hurt to read, use a normal for loop instead. Readability wins.

Unpacking

Assign multiple targets at once — Python destructures the right-hand side:

point = (3, 8)
x, y = point                # x=3, y=8
a, b = b, a                 # the idiomatic swap
first, *rest = [10, 20, 30, 40]   # first=10, rest=[20, 30, 40]
*init, last = range(5)            # init=[0, 1, 2, 3], last=4

Star-unpacking also merges collections: [*xs, *ys] concatenates lists, {**d1, **d2} merges dicts (later keys win).

Why this matters at work

Data-shaped code is everywhere: cleaning rows, reshaping API payloads, building lookup tables. Comprehensions make that intent visible instead of burying it in append-and-index bookkeeping.