Sets in Python#

🐍 Data Analysis Using Python 📚 Strings & Data Structures Lesson 025

Important

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Unordered collections of unique values#

The last core data structure is the set — an unordered collection of unique values. Sets excel at membership testing, removing duplicates, and set operations (union, intersection), and understanding them completes Python’s built-in structures. This lesson covers sets, closing the structures stage.

What a set is#

A set is a collection with two defining properties: its values are unique (no duplicates) and unordered (no positional index). Written with curly braces (like a dictionary, but values not pairs):

regions = {"North", "South", "East", "North"}   # duplicate ignored
print(regions)            # {"North", "South", "East"} — 3 unique values

Adding a duplicate has no effect (the value is already present), and there is no set[0] — sets are not indexed. Their purpose is membership and uniqueness, not order.

Set operations#

Sets support fast membership testing and mathematical set operations:

"North" in regions       # True — fast membership test

a = {1, 2, 3}
b = {2, 3, 4}
a | b                     # {1, 2, 3, 4} — union (in either)
a & b                     # {2, 3} — intersection (in both)
a - b                     # {1} — difference (in a but not b)

Membership testing (in) is very fast on a set — faster than searching a list — and the operations (union |, intersection &, difference -) answer “in either / both / one but not the other” directly. These are exactly the set operations of mathematics, and of SQL’s UNION/INTERSECT/EXCEPT.

Why sets matter#

Sets serve specific, common needs:

Removing duplicates — converting a list to a set drops duplicates instantly (set(my_list)), the fastest deduplication in Python.
Fast membership testing — checking whether a value is in a large collection is far faster with a set than a list, which matters at scale.
Comparing collections — the set operations answer “what is common / different between these two collections?” directly (which customers are in both lists? which are only in one?).

For these tasks — uniqueness, membership, comparison — the set is the right tool, cleaner and faster than working around a list.

The caveat#

Sets’ properties are also their limitations. Being unordered, a set cannot be indexed or sliced, and does not preserve insertion order — if order matters, a set is the wrong structure (use a list). Being unique, a set cannot hold duplicates — which is the point for deduplication, but means a set cannot represent data where repetition is meaningful (a set of sales figures would collapse identical amounts into one, losing information). And set elements must be immutable (like dictionary keys), so a set cannot contain lists. Use a set precisely when uniqueness and membership are what you want, and a list when order or repetition matters. This completes Python’s core data structures; the next lessons open the final stage — the libraries that make Python a data-analysis powerhouse.

Hint