Similar to how we can find elements in common between sets, we can also find unique elements in one set. To do so, the set or frozenset use the .difference() method or the - operator. This returns a set or frozenset, which contains only the elements from the first set which are not found in the second set. Similar to the other operations, the type of the first operand (a set or frozenset on the left side of the operator or method) determines if a set or frozenset is returned when finding the difference.

Take a look at the Venn diagram representing a difference operation that captures elements that are unique to set A:

Python Set Difference

Here is what finding a set difference looks like in Python:

# Given a set and frozenset of song tags for two python related hits prepare_to_py = {'rock', 'heavy metal', 'electric guitar', 'synth'} py_and_dry = frozenset({'classic', 'rock', 'electric guitar', 'rock and roll'}) # Find the elements which are only in prepare_to_py only_in_prepare_to_py = prepare_to_py.difference(py_and_dry) print(only_in_prepare_to_py)

Would Output:

{'heavy metal', 'synth'}

Alternativly, we can use the - operator:

# Find the elements which are only in py_and_dry only_in_py_and_dry = py_and_dry - prepare_to_py print(only_in_py_and_dry)

Would output:

frozenset({'rock and roll', 'classic'})

This operation also supports an updating version of the method. You can use .difference_update() to update the original set with the result instead of returning a new set or frozenset object.

Let’s see how we can apply this operation to our music application!



In order to try and increase the accuracy of your app’s song recommendations, we have decided to add some logic that will find the differences between liked and disliked songs. We will create another recommended dictionary of songs based on these differences.

Create a new variable called tag_diff that is the set difference between the tags inside of the one song of user_liked_song and the one song of user_disliked_song. Don’t forget to convert the list of tags into a set to perform this operation!


Now that you know the difference in tags between the liked song and disliked song, use those tags to find any songs from song_data which contain them.

Make sure not to include the liked and disliked songs. Store the newly found songs into a dictionary called recommended_songs.

Print recommended_songs to see the result!

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