Mrinal Singh Walia — Updated On May 31st, 2021
Beginner Listicle Programming Python

This article was published as a part of the Data Science Blogathon

 Sets are an example of the essence data structures of Python.


Data structures are the construction house for Python scripts. They hold or include the data carefully to make the workers operate more efficiently. Hence, it stands of fundamental importance to investigate how to cooperate with data structures.

A set is a combination of objects with the latter characteristic points

  • Sets are unordered
  • Sets hold distinct elements.
  • The elements in a set demand to be permanent, i.e., immutable

As sets are unordered, we cannot conduct operations like indexing and slicing on sets. A set does not allow to have mutable objects such as lists as an element in a python set. An ordinary use case of sets is to eliminate duplicate pieces from a combination or sequence.

In this report, we will discover 5 commonly used operations on sets. Let’s begin by generating a set. We can practice the set constructor on separate collections to build a set.

mylst = ['A', 'A', 'B', 'A', 'C', 'B']
myst = set(mylst)
Output: {'A', 'B', 'C'}

We have built a set from a list. The set only covers the unique elements in the list. The set constructor can also be implemented to a NumPy array. Here is the code for that:

import numpy as np
arr = np.random.randint(0, 5, size=20)
myst = set(arr)
Output: [4 0 4 3 1 1 3 0 0 1 3 4 0 3 2 4 1 4 3 3]
Output: {0, 1, 2, 3, 4}



1.Adding and removing elements

It is a straightforward method to join or eliminate elements. The add and remove methods are applied, respectively:

Output: {0, 1, 2, 3, 4, 5}

If we decide to append an element previously in the set, the set will remain the likewise, and we will not get a notice or failure.

Output: {0, 1, 2, 3, 4, 5}

The application of the remove method is identical.

Output: {0, 1, 2, 3, 5}

2.Updating a set

Updating a set with another set involves affixing the elements in the second set to the original set. Analyze the resulting two sets.

myst = set([0, 1, 2, 3, 5])
myotherst = set([3, 4, 5, 6, 7])

We can modernize “myst” by “myotherst” as follows:

Output: {0, 1, 2, 3, 4, 5, 6, 7}

The update approach is beneficial because we do not require to worry about the equivalent and diverse elements in both sets.

We can also renew a set with separate data structures, such as lists or tuples.

myst = set([0, 1, 2, 3, 5])
mylst = [1, 2, 10,11,12]
Output: {0, 1, 2, 3, 5, 10, 11, 12}

3.Combining sets

The update method operates in place, which implies it alters the primary set. In some circumstances, we need a mixture of various sets without renewing the original ones. The union system reflects the quality of two sets, so we can attach it to a different variable.

myst = {'A', 'B', 'C'}
newst = {'B', 'C', 'D', 'E'}
newst2 = {1, 2, 3}
combinedst = myst.union(newst).union(newst2)print(myst)
Output: {'A', 'B', 'C'}
Output: {'A', 1, 2, 'D', 'E', 3, 'B', 'C'}

We get a sequence (i.e. union) of the sets, but the first sets continue to be the same.

In the case above, we have also noticed a strange way of building a set: The parts can be appended inside the curly braces (“{}”) to the entire set.

4.Comparing sets

Two sets can be examined in terms of the elements they hold. The issuperset and issubset methods can be utilized to relate two sets.

Suppose we have two sets named A and B. If A includes all the ingredients in B, then A is a superset of B. In that event, B is a subset of A.

A = {1, 2, 3, 4, 5}
B = {1, 4, 5}
C = {1, 4, 6}
Output: True
Output: True
Output: False

One of the parts in set C is not in set A. Therefore, A is not a superset of C.

If two sets carry identical elements, they can be regarded as both the superset and subset of one another.

D = {1, 4, 5}
E = {1, 4, 5}
Output: True
Output: True

5.The intersection and difference

The theory of sets is pretty comparable to the Venn charts in mathematics(statistics).

We might be engaged in the elements that are in one set, just not in the opposite. Furthermore, we might require to discover the highlights that are in both sets. The diversity and intersection methods can be utilized to implement these actions, respectively.

A = {1, 2, 3, 4, 5}
B = {3, 4, 5, 6, 7, 8}
Output: {1, 2}
Output: {3, 4, 5}
Output: {6, 7, 8}

The distribution of the sets does not express when determining the intersection. However, the deviation is determined based on the order. The diversity of A from B includes elements in A but no in B and vice versa.


We have performed cases to illustrate 5 everyday operations conducted on sets. More methods can be utilized for sets. Nevertheless, what we have incorporated in this article is suitable for most maximum cases.

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