Nearby lessons

66 of 159

Python - Lists Python - Tuples Python - Sets Python - Dictionaries Python - List Comprehensions

Python - Sets

📌 What You Will Learn

Understand what a set is and how it stores only unique values
Create sets using set() and modify them with add(), update() and remove()
Remove elements with remove(), discard(), pop() and clear()
Apply mathematical operations like union, intersection, difference and symmetric difference
Use membership operators with sets
Write set comprehensions and compare sets with lists and tuples

If we want to represent a group of unique values as a single entity, then we should use a Set.

A Set is one of Python's built-in collection data types.

It automatically removes duplicate elements and stores only unique values.

A Set has the following characteristics:

Duplicate elements are not allowed.
Insertion order is not preserved.
We can sort the elements.
Indexing is not supported.
Slicing is not supported.
Heterogeneous elements are allowed.
Set objects are mutable.
Mathematical operations like Union, Intersection, Difference and Symmetric Difference are supported.

1. Duplicate Elements are Not Allowed

A Set stores only unique values.

If duplicate values are provided, Python automatically removes them.

2. Insertion Order is Not Preserved

A Set does not maintain the order in which elements are inserted.

Therefore, while printing a set, the output order may be different from the insertion order.

3. Heterogeneous Elements are Allowed

A Set can store different types of data together.

4. Set is Mutable

After creating a Set, we can add, remove and update elements.

5. Mathematical Operations are Supported

Python supports mathematical operations such as Union, Intersection, Difference and Symmetric Difference on Set objects.

🐍Code Cell

1	s = {10, "A", 20.5, True}
2
3	print(s)

Output

{10, 'A', 20.5, True}

The above Set contains different types of values:

Integer
String
Float
Boolean

Hence, heterogeneous elements are allowed in a Set.

A Set is represented using curly braces {}.

Each element is separated by a comma (,).

🐍Code Cell

1	s = {10, 20, 30, 40}

Output

No output captured.

There are different ways to create a Set in Python.

If the elements are already known, we can create a Set directly using curly braces.

🐍Code Cell

1	s = {10, 20, 30, 40}
2
3	print(s)
4
5	print(type(s))

Output

{40, 10, 20, 30}

Since insertion order is not preserved, the output order may be different each time the program runs.

We can also create a Set by using the set() function.

The argument can be any iterable object such as a List, Tuple, Range or String.

🐍Code Cell

1	set(any_iterable)

Output

No output captured.

🐍Code Cell

1	l = [10, 20, 30, 40, 10, 20, 10]
2
3	s = set(l)
4
5	print(s)

Output

{40, 10, 20, 30}

The duplicate values (10 and 20) are removed automatically.

Only unique values are stored in the Set.

🐍Code Cell

1	s = set(range(5))
2
3	print(s)

Output

{0, 1, 2, 3, 4}

While creating an empty Set, we must use the set() function.

Using empty curly braces creates an empty Dictionary, not a Set.

🐍Code Cell

1	s = {}
2
3	print(s)
4
5	print(type(s))

Output

{}

The expression {} creates an empty Dictionary.

It does not create an empty Set.

🐍Code Cell

1	s = set()
2
3	print(s)
4
5	print(type(s))

Output

set()

Expression	Creates
`{}`	Empty Dictionary
`set()`	Empty Set

Python provides several built-in methods to modify a Set.

In this part, we will learn the following methods:

add()
update()
copy()
pop()

The add() method is used to add a single element to a Set.

If the element already exists, Python does not add it again because duplicate values are not allowed.

🐍Code Cell

1	set_name.add(element)

Output

No output captured.

🐍Code Cell

1	s = {10, 20, 30}
2
3	s.add(40)
4
5	print(s)

Output

{40, 10, 20, 30}

The value 40 is added to the Set.

Since a Set does not preserve insertion order, the output order may be different.

🐍Code Cell

1	s = {10, 20, 30}
2
3	s.add(20)
4
5	print(s)

Output

{10, 20, 30}

The value 20 already exists in the Set.

Therefore, no duplicate element is added.

The update() method is used to add multiple elements to a Set.

The arguments passed to update() must be iterable objects.

Examples of iterable objects are:

List
Tuple
Range
Set
String

🐍Code Cell

1	set_name.update(iterable1)
2
3	set_name.update(iterable1, iterable2, ...)

Output

No output captured.

🐍Code Cell

1	s = {10, 20, 30}
2
3	l = [40, 50, 60, 10]
4
5	s.update(l, range(5))
6
7	print(s)

Output

{0, 1, 2, 3, 4, 40, 10, 50, 20, 60, 30}

The elements from the List and Range are added to the Set.

Duplicate values are removed automatically.

The display order may vary because Sets are unordered.

add()	update()
Adds one element.	Adds multiple elements.
Accepts only one argument.	Accepts one or more iterable objects.
The argument can be a single element.	Arguments must be iterable objects.

🐍Code Cell

1	s = {10, 20}
2
3	s.add(30)
4
5	print(s)

Output

{10, 20, 30}

🐍Code Cell

1	s = {10, 20}
2
3	s.add(30, 40, 50)

Output

TypeError: add() takes exactly one argument (3 given)

🐍Code Cell

1	s = {10, 20}
2
3	s.update(30)

Output

TypeError: 'int' object is not iterable

🐍Code Cell

1	s = {10, 20}
2
3	s.update(range(1, 10, 2), range(0, 10, 2))
4
5	print(s)

Output

{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20}

The copy() method returns a copy of the Set.

The copied Set is a new object containing the same elements.

🐍Code Cell

1	new_set = set_name.copy()

Output

No output captured.

🐍Code Cell

1	s = {10, 20, 30}
2
3	s1 = s.copy()
4
5	print(s1)

Output

{10, 20, 30}

The pop() method removes and returns a random element from the Set.

Since Sets do not preserve insertion order, we cannot predict which element will be removed.

🐍Code Cell

1	set_name.pop()

Output

No output captured.

🐍Code Cell

1	s = {40, 10, 30, 20}
2
3	print(s)
4
5	print(s.pop())
6
7	print(s)

Output

{40, 10, 20, 30}
40
{10, 20, 30}

The removed element is random.

Different executions may remove different elements because Sets are unordered.

In this part, we will learn the following Set methods:

remove()
discard()

Both methods are used to remove elements from a Set, but they behave differently when the specified element is not available.

The remove() method removes the specified element from a Set.

If the specified element is not present, Python raises a KeyError.

🐍Code Cell

1	set_name.remove(element)

Output

No output captured.

🐍Code Cell

1	s = {10, 20, 30}
2
3	s.remove(10)
4
5	print(s)

Output

{20, 30}

The element 10 is present in the Set.

Therefore, it is removed successfully.

Since Sets do not preserve insertion order, the output order may vary.

🐍Code Cell

1	s = {10, 20, 30}
2
3	s.remove(40)

Output

KeyError: 40

The element 40 is not available in the Set.

Therefore, Python raises a KeyError.

The discard() method also removes the specified element from a Set.

If the element is not present, no exception is raised.

🐍Code Cell

1	set_name.discard(element)

Output

No output captured.

🐍Code Cell

1	s = {10, 20, 30}
2
3	s.discard(20)
4
5	print(s)

Output

{10, 30}

The specified element is removed successfully.

No value is returned by the discard() method.

🐍Code Cell

1	s = {10, 20, 30}
2
3	s.discard(40)
4
5	print(s)

Output

{10, 20, 30}

The element 40 is not available in the Set.

The discard() method does nothing.

No exception is raised.

remove()	discard()
Removes the specified element.	Removes the specified element.
Raises `KeyError` if the element is not present.	Does not raise any error if the element is not present.
Best when the element definitely exists.	Best when the element may or may not exist.

These methods are commonly used in:

Student attendance systems.
Inventory management.
User permission management.
Removing duplicate records.
Data cleaning applications.

The clear() method removes all elements from the Set.

After calling this method, the Set becomes empty.

The Set object still exists, but it contains no elements.

🐍Code Cell

1	set_name.clear()

Output

No output captured.

🐍Code Cell

1	s = {10, 20, 30, 40}
2
3	print("Before clear():", s)
4
5	s.clear()
6
7	print("After clear():", s)

Output

Before clear(): {40, 10, 20, 30}
After clear(): set()

The clear() method removes every element from the Set.

The Set object is not deleted.

Only its contents are removed.

🐍Code Cell

1	s = {1, 2, 3}
2
3	s.clear()
4
5	print(type(s))
6
7	print(len(s))

Output

Even after using clear(), the variable still refers to a Set object.

Its length becomes 0.

Method	Purpose	Raises Error?
`pop()`	Removes one random element.	No
`remove()`	Removes a specified element.	Yes, if the element is not present.
`discard()`	Removes a specified element.	No
`clear()`	Removes all elements.	No

Situation	Recommended Method
Remove any one element.	`pop()`
Remove a known element.	`remove()`
Remove an element that may or may not exist.	`discard()`
Remove all elements.	`clear()`

These methods are commonly used in:

Removing inactive users.
Clearing cache data.
Inventory management systems.
Attendance management.
Data cleaning applications.

Method	Description
`add(x)`	Adds one element.
`update(x)`	Adds multiple elements.
`copy()`	Creates a copy of the Set.
`pop()`	Removes one random element.
`remove(x)`	Removes the specified element.
`discard(x)`	Removes the specified element without raising an error.
`clear()`	Removes all elements.

One of the biggest advantages of a Set is that it supports mathematical set operations.

These operations are useful for comparing two or more sets and finding common or unique elements.

Python supports the following mathematical operations:

union()
intersection()
difference()
symmetric_difference()

The union() method returns a new Set containing all unique elements from both Sets.

Duplicate elements are automatically removed.

The original Sets are not modified.

🐍Code Cell

1	set3 = set1.union(set2)
2
3	# or
4
5	set3 = set1 \| set2

Output

No output captured.

🐍Code Cell

1	s1 = {10, 20, 30}
2
3	s2 = {30, 40, 50}
4
5	print(s1.union(s2))

Output

{10, 20, 30, 40, 50}

🐍Code Cell

1	s1 = {1, 2, 3}
2
3	s2 = {3, 4, 5}
4
5	print(s1 \| s2)

Output

{1, 2, 3, 4, 5}

The intersection() method returns only the common elements available in both Sets.

🐍Code Cell

1	set3 = set1.intersection(set2)
2
3	# or
4
5	set3 = set1 & set2

Output

No output captured.

🐍Code Cell

1	s1 = {10, 20, 30, 40}
2
3	s2 = {30, 40, 50, 60}
4
5	print(s1.intersection(s2))

Output

{30, 40}

🐍Code Cell

1	s1 = {1, 2, 3}
2
3	s2 = {2, 3, 4}
4
5	print(s1 & s2)

Output

{2, 3}

The difference() method returns elements that are present in the first Set but not in the second Set.

🐍Code Cell

1	set3 = set1.difference(set2)
2
3	# or
4
5	set3 = set1 - set2

Output

No output captured.

🐍Code Cell

1	s1 = {10, 20, 30, 40}
2
3	s2 = {30, 40, 50}
4
5	print(s1.difference(s2))

Output

{10, 20}

🐍Code Cell

1	s1 = {1, 2, 3, 4}
2
3	s2 = {3, 4, 5}
4
5	print(s1 - s2)

Output

{1, 2}

The symmetric_difference() method returns elements that are present in either Set but not in both.

🐍Code Cell

1	set3 = set1.symmetric_difference(set2)
2
3	# or
4
5	set3 = set1 ^ set2

Output

No output captured.

🐍Code Cell

1	s1 = {10, 20, 30}
2
3	s2 = {30, 40, 50}
4
5	print(s1.symmetric_difference(s2))

Output

{10, 20, 40, 50}

🐍Code Cell

1	s1 = {1, 2, 3}
2
3	s2 = {3, 4, 5}
4
5	print(s1 ^ s2)

Output

{1, 2, 4, 5}

Membership operators are used to check whether an element exists in a Set.

Python provides two membership operators:

in
not in

🐍Code Cell

1	s = {10, 20, 30}
2
3	print(20 in s)
4
5	print(40 in s)
6
7	print(50 not in s)

Output

True
False
True

Method	Operator	Returns
`union()`	\|	All unique elements
`intersection()`	&	Common elements
`difference()`	-	Elements only in first Set
`symmetric_difference()`	^	Non-common elements

Mathematical Set operations are commonly used in:

Student attendance comparison.
Database record matching.
Product recommendation systems.
Finding common friends in social networks.
Data analysis and reporting.

Python supports Set Comprehension, which provides a short and simple way to create a Set.

It is similar to List Comprehension, but it creates a Set instead of a List.

Set Comprehension automatically removes duplicate values because a Set stores only unique elements.

🐍Code Cell

1	{expression for variable in iterable}

Output

No output captured.

🐍Code Cell

1	s = {x for x in range(1, 6)}
2
3	print(s)

Output

{1, 2, 3, 4, 5}

🐍Code Cell

1	s = {x*x for x in range(1, 6)}
2
3	print(s)

Output

{1, 4, 9, 16, 25}

🐍Code Cell

1	s = {x for x in range(1, 11) if x % 2 == 0}
2
3	print(s)

Output

{2, 4, 6, 8, 10}

🐍Code Cell

1	s = {x for x in range(1, 11) if x % 2 != 0}
2
3	print(s)

Output

{1, 3, 5, 7, 9}

🐍Code Cell

1	numbers = [10, 20, 10, 30, 20, 40]
2
3	s = {x for x in numbers}
4
5	print(s)

Output

{40, 10, 20, 30}

🐍Code Cell

1	languages = {"python", "java", "html"}
2
3	upper = {lang.upper() for lang in languages}
4
5	print(upper)

Output

{'PYTHON', 'JAVA', 'HTML'}

Requires less code.
Easy to understand.
Creates Sets quickly.
Automatically removes duplicate values.
Supports conditions using if.

Indexing depends on the position of elements.

Since a Set does not preserve insertion order, every element does not have a fixed position.

Therefore, indexing is not supported.

🐍Code Cell

1	s = {10, 20, 30}
2
3	print(s[0])

Output

TypeError: 'set' object is not subscriptable

Slicing is based on indexes.

Since indexing is not available for Sets, slicing is also not supported.

🐍Code Cell

1	s = {10, 20, 30, 40}
2
3	print(s[1:3])

Output

TypeError: 'set' object is not subscriptable

Feature	List	Tuple	Set
Order Preserved	Yes	Yes	No
Duplicate Elements	Allowed	Allowed	Not Allowed
Mutable	Yes	No	Yes
Indexing	Supported	Supported	Not Supported
Slicing	Supported	Supported	Not Supported
Representation	[]	()	{}

📝 Key Takeaways

A set stores unique values and automatically removes duplicates
Sets do not support indexing or slicing
add() and update() add elements while remove(), discard(), pop() and clear() remove them
Union (|), intersection (&), difference (-) and symmetric difference (^) compare sets
Set comprehensions build sets in one expression

🧠 Test Your Knowledge

48 Questions

Progress: 0 / 48