Python’s set.issuperset(set) returns True if all elements of the specified set argument are members of this set. It determines whether the set on which the method is called is the superset of the specified set.

Here’s a minimal example that checks whether set s is a superset of t:

>>> s = {'Alice', 'Bob', 'Carl'}
>>> t = {'Alice', 'Bob'}
>>> s.issuperset(t)
True

Let’s dive into the formal syntax of the set.issuperset() method.

set.issuperset(set)

Argument

Data Type

Explanation

set

A set or iterable

The set against which the elements of this set should be checked

Return Value of set.issuperset()

The return value of set.issuperset() is a Boolean whether the set on which it is called is the superset of the set argument.

Advanced Examples set.issuperset()

There are some subtleties you need to understand regarding the set superset method. Let’s dive into them by example!

We start with a simple and trivial example:

>>> {'Alice', 'Bob'}.issuperset({'Alice'})
True

π‘Β Can you also pass a list as an argument to the set.issuperset() method? The answer is yes—the method takes any iterable.

>>> {'Alice', 'Bob'}.issuperset(['Alice'])
True

A set is the superset of itself.

>>> s = {1, 2, 3}
>>> s.issuperset(s)
True

This also means that two empty sets are the supersets of each other.

>>> set().issubset(set())
True

Can we pass multiple set arguments into the set.issuperset() method? No! It only takes one argument.

>>> {1, 2, 3}.issuperset({1, 2}, {3})
Traceback (most recent call last):
File "<pyshell#4>", line 1, in <module>
{1, 2, 3}.issuperset({1, 2}, {3})
TypeError: issuperset() takes exactly one argument (2 given)

To fix this TypeError, pass only one set argument into the set.issuperset() method.

What is the Time Complexity of set.issuperset() in Python?

The worst-case runtime complexity of the set.issuperset() method for a set with n elements and a set argument with m elements is O(m) because you need to check for each set element whether it’s a member of the set on which the method is called.

You can see this in the following simple experiment where we run the set method multiple times for increasing set sizes:

I ran this experiment on my Acer Aspire 5 notebook (I know) with Intel Core i7 (8th Gen) processor and 16GB of memory. Here’s the code of the experiment:

import matplotlib.pyplot as plt
import time
sizes = [i * 10**5 for i in range(50)]
runtimes = []
for size in sizes:
s = set(range(size))
t = set(range(0, size, 2))
# Start track time ...
t1 = time.time()
s.issuperset(t)
t2 = time.time()
# ... end track time
runtimes.append(t2-t1)
plt.plot(sizes, runtimes)
plt.ylabel('Runtime (s)')
plt.xlabel('Set Size')
plt.show()

Other Python Set Methods

All set methods are called on a given set. For example, if you created a set s = {1, 2, 3}, you’d call s.clear() to remove all elements of the set. We use the term “this set” to refer to the set on which the method is executed.

Create and return a new set containing all elements of this set except the ones in the given set arguments. The resulting set has at most as many elements as any other.

Replace this set with the symmetric difference, i.e., elements in either this set or the specified set argument, but not elements that are members of both.

Update this set with all elements that are in this set, or in any of the specified set arguments. The resulting set has at least as many elements as any other.

While working as a researcher in distributed systems, Dr. Christian Mayer found his love for teaching computer science students.

To help students reach higher levels of Python success, he founded the programming education website Finxter.com. He’s author of the popular programming book Python One-Liners (NoStarch 2020), coauthor of the Coffee Break Python series of self-published books, computer science enthusiast, freelancer, and owner of one of the top 10 largest Python blogs worldwide.

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