Set data structure is an unordered collection with no duplicate elements. Using curly braces { } you can create a set. There is also a set() function you can use to create a Set. Since empty curly brace { } is used to create both Set and Dictionary, to create an empty set you have to use set() function.

Here is an example:

americas = {'Canada', 'United States', 'Mexico', 'Mexico'}

Output: {'United States', 'Canada', 'Mexico'}

You will notice that duplicate 'Mexico' is not added. Also please note that your output may be different from the one shown. It may or may not be in the same order. Print output order is not predictable. Hence the term unordered collection. If you want the sequence order to be maintained then you should use one of the ordered collection types.

You can also construct a set using the set() function. Here we create a set out of list elements.

americas = set(['Canada', 'United States', 'Mexico'])

Output: {'United States', 'Canada', 'Mexico'}

Another example of using the set function with a tuple element

americas = set(('Canada', 'United States', 'Mexico'))

Output: {'United States', 'Canada', 'Mexico'}

If you do not put the square braces or parenthesis and also remove comma between words, then a set is formed with all the letters in the words.

americas = set('Canada' 'United States' 'Mexico')

Output: {'c', 'e', ' ', 'i', 'd', 'n', 'M', 'o', 'S', 't', ',', 's', 'a', 'U', 'C', 'x'}

You will notice that the result is not only case sensitive but also duplicate letters are eliminated. Notice only one letter 'a' is present in the set and similarly other duplicate letters are eliminated.

Set Operations

Operation Example Result Comments
add an element add
americas.add('Puerto Rico')
{'United States', 'Canada', 'Puerto Rico'} Given element is added. Order could be different
add multiple elements update
americas.update(('Puerto Rico','Cuba'))
{'United States', 'Canada', 'Puerto Rico', 'Cuba'} Comma separated multiple elements are added
remove an element remove
americas.remove('Puerto Rico')
{'United States', 'Canada'} Given element is removed. If the item to remove does not exist, remove() will raise an error.
remove an element discard
americas.discard('Puerto Rico')
{'United States', 'Canada'} Given element is removed. If the item to discard does not exist, discard() will not raise an error.
remove and get a random element pop
Any element may be removed Removed element is returned from this function
Empty the set clear
{} Set is emptied

Operations on Two Sets

Let us consider another set of three countries which were English colonies:

old_eng_col = {'Canada', 'India', 'Australia', 'United States'}

Then, here are some of the common set operations you can perform on both the sets and their results:

Operation Example Result Comments
intersection; &
americas & old_eng_col
{'United States', 'Canada'} A new set is created with the elements which are common in both sets. Note two ways of getting the same result
union; l
americas | old_eng_col
{'United States', 'India', 'Australia', 'Canada', 'Mexico'} A new set with all unique elements from both sets. Here also you can use union function instead of using l
difference; -
americas - old_eng_col
{'Mexico'} Elements in americas but not in old_eng_col. Here also you can use difference function instead of -
False Returns True if americas is a subset of old_eng_col

Here are a few more functions on sets:

  • copy() - Returns a copy of the set
  • difference_update() - Removes the items in this set that are also included in another, specified set
  • intersection_update() - Removes the items in this set that are not present in other, specified set(s)
  • isdisjoint() - Returns whether two sets have a intersection or not
  • issubset() - Returns whether another set contains this set or not
  • issuperset() - Returns whether this set contains another set or not
  • symmetric_difference() - Returns a set with the symmetric differences of two sets
  • symmetric_difference_update() - inserts the symmetric differences from this set and another

Common Operations across All Collections

Consider the code below to work with the table describing the operations below:

country_codes = {'US':'United States', 'UK':'United Kingdom', 
                'CA':'Canada', 'MX':'Mexico'}  # a dictionary
a = [10, 4, 1, 11]  # a list
b = (5, 1, 9, 5)  # a tuple
c = {5, 9, 1}  # a set
Operations Example Output Comments
Sort method sorted sorted_keys = sorted(country_codes)
['CA', 'MX', 'UK', 'US] A sorted list of keys is returned
Sort method sorted sorted_list = sorted(a)
[1, 4, 10, 11] A sorted list is returned
Sort method sorted sorted_tuple = sorted(b)
(1, 5, 5, 9) A sorted tuple is returned
Sort method sorted sorted_set = sorted(c)
{1,5,9} A sorted set is returned

There are many other functions like sorted that can be applied across all types of collections. E.g., len (to find the size of the collection), min (get min value), max (get max value)_ etc.. For a dictionary the operations are performed on the keys.

Unpacking Elements of a Collection

Elements of any collection can be unpacked into individual variables. This is very useful when you want to unpack all the elements at once. The below code unpacks the tuple containing top student's individual values into separate values:

top_student = ('jane', 'doe', 99, 21, 'f')
first_name, last_name, score, age, gender = top_student



The same operation works on List, Set and also Dictionary. In case of dictionary the unpacked value would be the keys only and not the key/value pair.

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