Python Sets
You will learn everything you need to know about Python sets in this tutorial, including how they are created, how they can be added to and removed from, and how they can be manipulated.
Python Sets – What is it?
Python sets are lists of items in an unordered arrangement. It is imperative that all elements of a set are unique (no duplicates) as well as immutable (unchangeable).
Sets themselves are mutable, however. Adding and removing items is possible.
The Set data type in Python is one of four built-in data types designed to store groups of data. The other three are List, Tuple, and Dictionary.
Union, intersection, symmetric difference, and other mathematical set operations can also be performed using sets.
Sets are collections that are both unindexed and unordered.
Curly brackets {} are used to indicate that a set is a collection.
Make a Python set by following simple steps:
Example
Note: It should be noted that sets are unordered, so you can never be sure in which order the items will appear in the set.
Unchangeable
After a Python sets is created, its contents cannot be changed, making them unchangeable.
In a set, you cannot change the items, but you can add new ones.
type() In Python Sets
A set is defined by Python as an object with the data type set:
How do you define a set’s data type?
Example
Unordered
Sets that are unordered do not have an established order.
It is not possible to refer to set items by index or key, as they appear in a different order every time you use them.
Duplicates Not Allowed
It is not possible to have two items with the same value in a set.
There will be no duplicate values accepted:
Example
Python Sets Items
Python sets are unordered, unchangeable, and do not allow duplicate values to be stored, when it comes to the items in the set.
The types of data in set items
Items in a set can be of any type of data, such as: String, int and boolean data types:
Boolean, int, and string data types:
Example
Different types of data can be contained within a set:
A Python set containing strings, integers, and booleans:
Example
What is the length of a set?
Python sets are counted by the len() method, which returns the number of items in the set.
A set has how many items:
Example
The set() Constructor
To make a set, you can also use the set() constructor.
Making a set with the set() constructor:
Example
Creating an empty sets
An empty curly brace {} will give a Python empty dictionary. We use the set() function without any arguments to make a set without any elements.
Empty sets should be distinguished from dictionaries:
Example
Collections (Arrays) in Python
The Python programming language supports four types of data collection:
- Lists are ordered collections that can be changed. Members can be duplicated.
- The tuple is an ordered collection that cannot be changed. Duplicate members are allowed.
- The term set refers to a collection that is unordered and not indexed. Members should not be duplicated.
- A dictionary is a collection of entries that are ordered and changeable. Members are not duplicated.
Since Python version 3.7, dictionaries are ordered. Dictionaries are unordered in Python 3.6 and earlier.
Taking into account the properties of a collection type is very useful when choosing a collection type.
By selecting the right type for a particular data set could mean retaining the meaning as well as improving efficiency, security, or even improving the quality of the data.
Python sets methods
Python sets method has many variations, some of which have already been used above.
A list of all the methods available with the set objects is provided below in order of their availability:
| Method | Overview |
|---|---|
| add() | This method adds a new element to the set of elements. |
| clear() | All elements are removed from the set. |
| copy() | The set is returned as a copy. |
| difference() | A set is removed from another set by removing all its elements. |
| difference_update() | Removes a member element from the set. (Do nothing if the element isn’t in the set) |
| discard() | Created set created by intersecting two sets. |
| intersection() | The intersection of the set with another is updated. |
| intersection_update() | Whether two sets intersect at null. |
| isdisjoint() | It returns True if the given set is contained within another set. |
| issubset() | Returns True if another set exists. |
| issuperset() | A set element is removed and returned. Empty sets raise KeyError |
| pop() | Removes a set element. |
| remove() | A KeyError is raised if the element is not a member. |
| symmetric_difference() | A new set is returned when two sets are symmetrically different. |
| symmetric_difference_update() | A set is updated with the symmetric difference between itself and another set. |
| union() | The union of sets is a new set. |
| update() | Creates a distinct set by combining itself with other sets. |
Python Sets Advantages
Here are five advantages of using sets in Python:
- Sets ensure that each element appears only once. This property makes sets ideal for tasks such as removing duplicates from a list or checking membership of an item in a collection without worrying about duplicates.
- Sets have highly efficient membership testing operations. Checking whether an element exists in a set is much faster than searching through a list or tuple. This advantage becomes more pronounced as the size of the set grows.
- Sets provide built-in operations for set theory, such as union, intersection, difference, and symmetric difference. These operations make it easy to perform set-related calculations, filtering, or comparisons.
- Python offers both mutable and immutable versions of sets. The mutable set (
set) allows you to add or remove elements, while the immutable version (frozenset) provides a hashable and immutable variant of sets that can be used as keys in dictionaries or elements in other sets. - Sets can be easily iterated over using loops. The order of iteration is not guaranteed, but you can efficiently access each element in the set for processing or analysis.
