{"id":75102,"date":"2020-01-10T12:00:50","date_gmt":"2020-01-10T06:30:50","guid":{"rendered":"https:\/\/techvidvan.com\/tutorials\/?p=75102"},"modified":"2020-01-10T12:00:50","modified_gmt":"2020-01-10T06:30:50","slug":"python-sets","status":"publish","type":"post","link":"https:\/\/techvidvan.com\/tutorials\/python-sets\/","title":{"rendered":"Python Sets – The collection of unordered and unindexed Python objects"},"content":{"rendered":"

Till now, we saw various data types in Python which include numbers<\/strong>, strings<\/strong>, lists<\/strong>, tuples<\/strong>, and dictionaries<\/strong>.<\/p>\n

Today, we are going to see another data type that is Python Sets<\/strong>. We will see what sets are and how you can create<\/strong>, access<\/strong> and perform operations<\/strong> on them. We will also see the functions associated with them.<\/p>\n

So let’s start with the concept of Python sets.<\/p>\n

Python Sets<\/h3>\n

Sets in Python are a collection<\/strong> of unordered<\/strong> and unindexed<\/strong> Python objects<\/strong>. Sets are mutable<\/strong>, iterable<\/strong> and they do not<\/strong> contain duplicate values<\/strong>. It is similar to the concept of the mathematical set<\/strong>.<\/p>\n

Create a Set in Python<\/h3>\n

The elements in a set are declared inside curly braces<\/strong> separated by commas<\/strong>.<\/p>\n

Code:<\/strong><\/p>\n

#sets are unordered, it rearranges itself in sorted order\na = {1, 2, 7, 5, 5}\nb = {99, 100, 200, \u201cyes\u201d, \u201cno\u201d}\n\n#empty set are created using the set() function\nempty_set = set()\nprint(empty_set)\nprint(type(empty_set))\nempty_dictionary = {}\nprint(type(empty_dictionary))<\/pre>\n
Output:<\/strong><\/p>\n
set()
\n<class \u2018set\u2019 >
\n<class \u2018dict\u2019 ><\/div>\n
Accessing Python Sets<\/h3>\n
As we discussed earlier that sets in Python are unindexed<\/strong>, so we access the entire set at once in sorted order<\/strong>.<\/p>\n
Code:<\/strong><\/p>\n
a = {7, 6, 5, 4, 3, 4, 5, 6}\nprint(a)<\/pre>\n
Output:<\/strong><\/p>\n
{3, 4, 5, 6, 7}<\/div>\n
See how the elements are sorted<\/strong> and duplicates<\/strong> are reduced in sets.<\/p>\n
Since the sets are unindexed<\/strong>, we cannot<\/strong> use slicing<\/strong> or indexing<\/strong> on sets, it will give us a TypeError<\/strong> error when using indexing<\/strong>.<\/p>\n
Code:<\/strong><\/p>\n
a = {1,2,3,4}\nprint(a[2])<\/pre>\n
Output:<\/strong><\/p>\n
Traceback (most recent call last):
\n\u00a0\u00a0<\/b>File “<stdin>”, line 2, in <module>
\nTypeError: ‘set’ object does not support indexing<\/div>\n
Iterating on Sets in Python<\/h3>\n
In python sets, they are iterable<\/strong> and we can iterate over them with a \u2018for\u2019 loop<\/strong>. Don\u2019t expect the order of elements<\/strong> to be the same as the original<\/strong>.<\/p>\n
Code:<\/strong><\/p>\n
ratings = {\u2018A\u2019, \u2018B\u2019, \u2018C\u2019, \u2018D\u2019}\nfor rate in ratings:\n  print(rate)<\/pre>\n
Output:<\/strong><\/p>\n
B
\nD
\nA
\nC<\/div>\n
Deleting Elements from Python Sets<\/h3>\n
In python sets, to delete the element, we usually have to specify the element<\/strong> we want to delete. The functions used for deleting element are discard()<\/strong>, remove()<\/strong>, pop()<\/strong> and clear()<\/strong>.<\/p>\n
1. discard(element)<\/h4>\n
The function deletes<\/strong> the specified element<\/strong> from the set and if the element is not present<\/strong> in the set, it does not<\/strong> return any error<\/strong>.<\/p>\n
Code:<\/strong><\/p>\n
a={1,2,3,4}\na.remove(3)\nprint(a)<\/pre>\n
Output:<\/strong><\/p>\n
{1, 2, 4}<\/div>\n
2. remove(element)<\/h4>\n
This function is similar to discard()<\/strong> but the difference is that it throws an error<\/strong> when the specified element to delete is not found<\/strong> in the set.<\/p>\n
Code:<\/strong><\/p>\n
a={10,20,30}\na.remove(10)\nprint(10)<\/pre>\n
Output:<\/strong><\/p>\n
{20, 30}<\/div>\n
3. pop()<\/h4>\n
pop() function<\/strong> doesn\u2019t take any argument and it removes<\/strong> an arbitrary element<\/strong> from the set and also returns<\/strong> the deleted element<\/strong>.<\/p>\n
Code:<\/strong><\/p>\n
num_set={10,8,6,4}\ndeleted = num_set.pop()\nprint(deleted)\nprint(num_set)<\/pre>\n
Output:<\/strong><\/p>\n
8
\n{10,4,6}<\/div>\n
4. clear()<\/h4>\n
The clear() function empties<\/strong> the set. It can also be used for dictionaries<\/strong>.<\/p>\n
Code:<\/strong><\/p>\n
a = {1,5,10,15,20}\na.clear()\nprint(a)<\/pre>\n
Output:<\/strong><\/p>\n
set()<\/div>\n
As we know, an empty set<\/strong> is denoted as set()<\/strong> and not <\/strong>by {}<\/strong>.<\/p>\n
Updating a Set in Python<\/h3>\n
Set are mutable<\/strong> so we can remove elements<\/strong> and also add<\/strong> or update new elements<\/strong> to the set. We have two functions<\/strong> to update the values in the set. These are add()<\/strong> and update()<\/strong> methods.<\/p>\n
Let\u2019s look at each of them.<\/p>\n
1. add(element)<\/h4>\n
The add()<\/strong> function in python sets takes the value as an argument<\/strong> which we want to add to our set and adds<\/strong> the element in<\/strong> our set<\/strong>.<\/p>\n
Code:<\/strong><\/p>\n
fruits = {\u201capple\u201d, \u201cbanana\u201d,\u201dcherry\u201d}\nfruits.add(\u201cstrawberry\u201d)\nprint(fruits)<\/pre>\n
Output:<\/strong><\/p>\n
{‘apple’, ‘banana’, ‘cherry’, ‘strawberry’}<\/div>\n
2. update(updated_set)<\/h4>\n
The update()<\/strong> function in python sets can be used to add multiple values<\/strong> to the set at once. It takes an iterable<\/strong> like lists<\/strong>, set<\/strong> or tuple<\/strong> as an argument.<\/p>\n
Code:<\/strong><\/p>\n
fruits = {\u201capple\u201d, \u201cbanana\u201d,}\nfruits.update([1,2,3])\nprint(fruits)\nfruits.update({\u2018cherry\u2019, \u2019kiwi\u2019 })<\/pre>\n
Output:<\/strong><\/p>\n
{1, 2, 3, ‘apple’, ‘banana’}
\n{1, 2, 3, ‘apple’, ‘banana’, \u2018kiwi\u2019}<\/div>\n
Frozen Sets in Python<\/h3>\n
So far we have seen sets are mutable<\/strong>, but now I am going to tell you about immutable<\/strong> sets.<\/p>\n
Frozen sets are similar to sets but the only difference is that frozen sets are immutable<\/strong>.<\/p>\n
Let\u2019s see how we can create a frozen set:<\/p>\n
The frozenset()<\/strong> inbuilt function is used to create<\/strong> a frozen set<\/strong>. We can pass any iterable like list<\/strong>, set<\/strong>, tuple<\/strong>, etc and it will return us an immutable set of class type<\/strong> frozenset<\/strong>.<\/p>\n
Syntax:<\/strong><\/p>\n
frozenset(iterable)<\/pre>\n
Example: <\/strong><\/p>\n
immutabl_set = frozenset((2,4,2,1,2,3))\nprint(immutable_set)<\/pre>\n
Output:<\/strong><\/p>\n
frozenset({1, 2, 3, 4})<\/div>\n
Note:<\/strong> Since frozenset<\/strong> are immutable<\/strong> sets, we can\u2019t update<\/strong> or change<\/strong> any index values<\/strong>, the interpret will give us an error<\/strong> if we try to change them.<\/p>\n
Python Set Operations<\/h3>\n
In python sets, we can perform various operations on set using functions len()<\/strong>, max()<\/strong>, min()<\/strong>, any()<\/strong>, all()<\/strong>, sorted()<\/strong>.<\/p>\n
Let\u2019s look at each of these functions working.<\/p>\n
1. len()<\/h4>\n
The len() function is useful to know the number of elements<\/strong> inside an iterator<\/strong>. This function will return us the length<\/strong> of the set.<\/p>\n
Code:<\/strong><\/p>\n
a={1,2,2,3,3,4}\nprint(len(a))<\/pre>\n
Output:<\/strong><\/p>\n
4<\/div>\n
2. max() and min()<\/h4>\n
Functions like max()<\/strong> and min()<\/strong> require the set to contain the same data type<\/strong> elements to work with because we can\u2019t compare integers<\/strong> and strings<\/strong> directly.<\/p>\n
The function max() returns the maximum<\/strong> value<\/strong> from the set and the min() function returns the minimum<\/strong> value<\/strong>.<\/p>\n
Code:<\/strong><\/p>\n
a={1,2,3,5,2,9,10}\nprint( max(a))\nprint( min(a))<\/pre>\n
Output:<\/strong><\/p>\n
10
\n1<\/div>\n
3. any() and all()<\/h4>\n
These functions return us True<\/strong> or False<\/strong>. The any() function<\/strong> is used to check if any one of the values is True<\/strong> or not while the all() function<\/strong> will return True if all values result to boolean True<\/strong>.<\/p>\n
Code:<\/strong><\/p>\n
print(any({0, False}) )\nprint(all({1, \u2018Hello\u2019, True}) )<\/pre>\n
Output: <\/strong><\/p>\n
False
\nTrue<\/div>\n
4. sorted()<\/h4>\n
The sorted() function takes an iterable<\/strong> like sets and sorts<\/strong> the elements<\/strong> in ascending order<\/strong>. It preserves<\/strong> the original array<\/strong> and returns<\/strong> a new array<\/strong> after sorting.<\/p>\n
Code:<\/strong><\/p>\n
sorted({6,5,4,3,4,5,2,1,4})<\/pre>\n
Output: <\/strong><\/p>\n
[1, 2, 3, 4, 5, 6]<\/div>\n
Now, we will see some mathematical functions as we discussed that Python sets are related to mathematical set concept<\/strong>.<\/p>\n
5. union()<\/h4>\n
Let\u2019s take an example of two sets A and B. The union operation between A and B will return<\/strong> all the distinctive elements<\/strong> in A<\/strong> and B<\/strong>.\"python<\/a><\/p>\n
Code:<\/strong><\/p>\n
A= {1, 2, 3, 4} ; B= {2, 4, 6, 4}\nprint( A.union(B) )<\/pre>\n
Output:<\/strong><\/p>\n
{1, 2, 3, 4, 6}<\/div>\n
6. intersection()<\/h4>\n
The intersection operation returns<\/strong> the set of elements<\/strong> that are common<\/strong> in the sets that are used<\/strong> in intersection<\/strong>.\"python<\/a><\/p>\n
Code:<\/strong><\/p>\n
A= {1, 2, 3, 4} ; B= {2, 4, 6, 4}\nprint( A.intersection(B) )<\/pre>\n
Output:<\/strong><\/p>\n
{ 2, 4 }<\/div>\n
To operate on more sets, you can pass them with commas<\/strong> in an argument.<\/p>\n
Code:<\/strong><\/p>\n
print({1,2,3}.intersection({2,3,4,5}, {1,3,6}) )<\/pre>\n
Output:<\/strong><\/p>\n
{3}<\/div>\n
7. difference()<\/h4>\n
The difference()<\/strong> function on sets performs the set difference operation<\/strong> on two<\/strong> or more sets<\/strong>.\"difference<\/a><\/p>\n
Code:<\/strong><\/p>\n
print({1,2,3,4}.difference({3,4,5,6} ) )\nprint({1,2,3,4}.difference({3,4,5,6},{ 1,3,4,5} ) )<\/pre>\n
Output: <\/strong><\/p>\n
{1, 2}
\n{2}<\/div>\n
8. symmetric_difference()<\/h4>\n
The function returns the unique elements<\/strong> in each set.\"symmetric<\/a><\/p>\n
Code:<\/strong><\/p>\n
print({1,2,4,5,6}.symmetric_difference({ 4, 5, 6, 7, 8 }) )<\/pre>\n
Output:<\/strong><\/p>\n
{1, 2, 7, 8}<\/div>\n
9. intersection_update(), difference_update() and symmetric_difference_update()<\/strong><\/h4>\n
As we saw earlier that these functions return a new set and do not change<\/strong> the original set<\/strong>. So these functions will perform<\/strong> and update<\/strong> the original set<\/strong>.<\/p>\n
Code:<\/strong><\/p>\n
set1 = {1,2,3} ; set2 = {2,3,4}\nset1.intersection_update(set2)\nprint(set1)\nset1.difference_update(set2)\nprint(set1)\nset1.symmetric_difference_update(set2)\nprint(set1)<\/pre>\n
Output:<\/strong><\/p>\n
{2,3}
\nset()
\n{2,3,4}<\/div>\n
10. isdisjoint()<\/h4>\n
The isdisjoint()<\/strong> function takes only one argument<\/strong> and checks<\/strong> whether the two sets<\/strong> are completely different<\/strong> or not. In other words, their intersection<\/strong> should be null<\/strong>.<\/p>\n
Code:<\/strong><\/p>\n
print( {1,2,3}.isdisjoint( {5,6,7} ) )\nprint( {1,2,3}.isdisjoint( {5,2,7} ) )<\/pre>\n
Output:<\/strong><\/p>\n
True
\nFalse<\/div>\n
11. issubset()<\/h4>\n
The issubset() function<\/strong> returns True<\/strong> if the set is a proper subset<\/strong> of the set given in argument.<\/p>\n
Code:<\/strong><\/p>\n
print( {1,2,3}.issubset( {1,2,3,5,6,} ) )\nprint( {1,2,8}.issubset( {1,2,3,5,6,} ) )<\/pre>\n
Output:<\/strong><\/p>\n
True
\nFalse<\/div>\n
12. issuperset()<\/h4>\n
Like the issubset()<\/strong> method, this function returns True<\/strong> when the set is a superset<\/strong> of the set given in argument.<\/p>\n
Code:<\/strong><\/p>\n
print( {1,2,3}.issuperset( {1, 2} ) )\nprint( {1,2,3}.issuperset( {1, 4} ) )<\/pre>\n
Output:<\/strong><\/p>\n
True
\nFalse<\/div>\n
13. Membership<\/h4>\n
In python sets, the python operators \u2018in\u2019<\/strong> and \u2018not in\u2019<\/strong> are called membership operators<\/strong> that checks if an element is present<\/strong> in the collections of elements<\/strong>.<\/p>\n
Code:<\/strong><\/p>\n
print( \u201cCherry\u201d in {\u201cApple\u201d, \u201cCherry\u201d, \u201cKiwi\u201d} )\nprint( \u201cCherry\u201d not in {\u201cApple\u201d, \u201cCherry\u201d, \u201cKiwi\u201d} )<\/pre>\n
Output:<\/strong><\/p>\n
True
\nFalse<\/div>\n
Summary<\/h3>\n
To sum up, we first saw Python sets which holds unique values and then saw how to access<\/strong> them, delete<\/strong> and update<\/strong> the elements in sets<\/strong>. Later on, we discussed all the operations<\/strong> you can perform on Python sets.<\/p>\n
So that was all about TechVidvan’s Python sets article.<\/p>\n","protected":false},"excerpt":{"rendered":"
Till now, we saw various data types in Python which include numbers, strings, lists, tuples, and dictionaries. Today, we are going to see another data type that is Python Sets. 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