Python Set Data Structure in Depth

By | 2018-08-20T17:37:58+00:00 April 19th, 2018|Python|0 Comments

In this tutorials, we will see in-depth details about Python Set Data Structure.

Python Set Data Structure:

Python Set represents a group of unique elements. If you wish to represent a group of unique elements into a single entity, then you can go with Python Set.

Characteristics of Set in Python:

  • The Set doesn’t allow duplicate elements.
  • It doesn’t preserve the insertion order.
  • We can store the heterogeneous elements in a Set.
  • Set objects are mutable, hence we can change the elements in a Set whenever we need it.

Creating Set :

You can create a Set using curly braces {} and set() function.

Example:

#set
s = {10,20,30,40}
print(type(s))

#set with hererogenious elements
s2 = {10,'A',"B",10.5}
print(type(s2))

#set with character elements
values = ['A','B','C','D']
s3 = set(values)
print(type(s3))

#set created uisng set() function
s4 = set(range(5))
print(type(s4))

Output:

<class 'set'>
<class 'set'>
<class 'set'>
<class 'set'>

Creating empty set is something tricky, we do not use empty curly braces {} to create empty Set, you do so, it will create python directory for you instead of empty Set. If you really want empty set go for set() function.

#It will give dict type
s = {}
print(type(s))

# It will create an emtpy set
s2=set()
print(type(s2))

Output:

<class 'dict'>
<class 'set'>

Adding elements to Set:

You can add elements to set using add() or update() functions. add() function is used to add a single element at a time, whereas update() function is used to add multiple elements at a time.

update() function can take list, tuple, string as an argument and will add it to the set.

s1=set()
s1.add(10)
s1.add(20)
print(s1)

#Adding multiple elements at a time

list = ['H','E','L','L','O']
s1.update(list)

print("Updated Set : ",s1)

#Adding elements with range() function

s1.update(range(1,5),range(5,10,1))
print("Updated with range : ",s1)

Output:

As we discussed earlier, Python Set doesn’t preserve insertion order, we can’t expect the outcome as we insert.

{10, 20}
Updated Set :  {'L', 'O', 10, 'E', 'H', 20}
Updated with range :  {1, 2, 3, 4, 5, 'L', 'O', 6, 7, 10, 8, 9, 'E', 'H', 20}

Removing Elements from Set:

You can remove an element from Set using 4 different functions.

  • remove()
  • discard()
  • pop() and
  • clear()

remove()

remove(x) function is used to remove a specific element from the Set. If the given element is not found in the set, it will throw KeyError.

s1={10,20,30,40,50}
s1.remove(20)
print("After Removing 20 : ",s1)

#Removing unknown element
s1.remove(70)

Output:

After Removing 20 :  {40, 10, 50, 30}

Traceback (most recent call last):
  File ".\sample.py", line 6, in <module>
    s1.remove(70)
KeyError: 70

discard(x)

It removes the given element from the Set. The only difference between remove() and discard() is – If the given element not found in the Set, remove() function throw KeyError, whereas discard() doesn’t give any error.

s1={10,20,30,40,50}
s1.discard(50)
print("After discard 50 : ",s1)

#discard unknown element
s1.discard(100)
print("After discard 100 : ",s1)

Output:

After discard 50 :  {40, 10, 20, 30}
After discard 100 :  {40, 10, 20, 30}

pop()

pop() removes and returns some random element from the Set.

s1 = {1,'a',2,4,5,8,9}
print("poped element : ",s1.pop())

print("After pop() the set wil be : ")
print(s1)

Output:

poped element :  a
After pop() the set wil be :
{1, 2, 4, 5, 8, 9}

clear()

clear() function is used to remove all elements from the Set.

s1 = {1,'a',2,4,5,8,9}
print("Elements in Set: ",s1)

s1.clear()
print("After clearing ")
print(s1)

Output:

Elements in Set:  {1, 2, 4, 5, 8, 9, 'a'}
After clearing
set()

Python Set operations:

Set can be used in mathematical operations like union, intersection, difference and symmetric difference. Pythons gave different functions to handle these operations.

Union():

Let’s consider the below two sets:

s1 = {10,20,30,40,50}
s2 = {60,70,50,80,10}

union of s1 and s2 will produce a new set of all elements from both s1 and s2 sets. This operation also achieved using | (or) operator.

Example:

s1 = {10,20,30,40,50}
s2 = {60,70,50,80,10}
print("union of s1, s2 : ",s1.union(s2))

print("s1 | s2         : ",s1|s2)

Output:

union of s1, s2 :  {70, 40, 10, 80, 50, 20, 60, 30}
s1 | s2         :  {70, 40, 10, 80, 50, 20, 60, 30}

intersection():

The intersection of two different sets (s1,s2) will produce a new set of elements which are common in both sets. It is also achieved using & (and) operator.

Example:

s1 = {10,20,30,40,50}
s2 = {60,70,50,80,10}
print("intersection of s1, s2 : ",s1.intersection(s2))

print("s1 & s2         : ",s1&s2)

Output:

intersection of s1, s2 :  {10, 50}
s1 & s2                :  {10, 50}

difference():

difference() function returns the elements which are present in s1 but not in s2. It is also achieved using – (minus) operator.

Example:

s1 = {10,20,30,40,50}
s2 = {60,70,50,80,10}
print("difference of s1, s2 : ",s1.difference(s2))
print("s1 - s2              : ",s1-s2)

Output:

difference of s1, s2 :  {40, 20, 30}
s1 - s2              :  {40, 20, 30}

symmentric_difference():

The symmentric_difference function returns the elements which are present in either s1 or s2 but not in both. It can be also achieved using ‘^’ operator.

Example:

s1 = {10,20,30,40,50}
s2 = {60,70,50,80,10}
print("symmetric_difference of s1, s2 : ",s1.symmetric_difference(s2))
print("s1 ^ s2                        : ",s1^s2)

Output:

symmetric_difference of s1, s2 :  {80, 20, 70, 40, 60, 30}
s1 ^ s2                        :  {80, 20, 70, 40, 60, 30}

Set Membership operations:

We can apply membership operators on python set data structure. The membership operators – in, not: are used to test whether a given element exists in the set or not.

Example:

s1 = {10,20,30,40,50}

print("is 10 in s1 ? ", 10 in s1)
print("is 60 in s1 ? ", 60 in s1)

Output:

is 10 in s1 ?  True
is 60 in s1 ?  False

Examples for Python Set:

References:

Happy Learning 🙂

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