The included jupyter notebook purges rules/associations that are duplicates by simple pandas duplication removal procedure.
The return dataframe from the association_rules contains two columns with our original sets of products => antecedents & consequents.
The frozensets are first converted into normal sets then merged & sorted stringified.
duplicates are then handled by pandas drop_duplicates.
This solution might not work for all scenarios & might also have performance issues with 1000's of products and sales overtime.