Sets provide powerful, efficient functionality that every Python programmer should have in their toolkit. As an unordered, mutable collection of unique elements, sets shine when you need to store non-repeating objects.
In this guide, I‘ll provide you with an in-depth master class on sets from the perspective of an experienced practitioner. You‘ll gain the expertise to leverage sets for faster, simpler, more Pythonic code.
Introduction to Python Sets
Let‘s start from square one – what exactly is a set in Python?
A set consists of an unordered collection of distinct, immutable objects. The key properties differentiating sets from lists and dictionaries are:
- Sets are unordered – there are no indices or key-value pairs
- Sets contain unique elements – no repeats or duplicates
- Set elements must be immutable – like strings, numbers, tuples
- Sets are mutable overall – you can modify them after creation
- Sets can be very fast and efficient for lookups
Think of sets like a mathematical sets from set theory. A Python set could represent the set of all odd numbers, the vowels from the English alphabet, or a collection of word tokens from a document.
The unique and unordered nature of sets delivers advantages over lists and dictionaries when:
- Removing duplicate entries from a collection
- Quickly checking membership of large datasets
- Performing common set operations like unions and intersections
- Analyzing relationships between datasets
Later we‘ll explore some code examples showing these use cases in practice.
First, let‘s get into more detail on how sets work under the hood…
Python Set Fundamentals Explained
Python sets provide extremely fast lookup speeds because they rely on a technique called hashing…
