Market-basket: calculating support/confidence/lift/rules How can I calculate support/confidence/lift on a dataset in order to find frequent itemsets and determine association rules, in python? What would be the most effective method for predicting and offering recommendations on a test set of incomplete "shopping carts"? I am limited to the Anaconda distribution so I cant use packages such as orange3, etc.
I've done this (using anaconda) with the following libraries. 

from mlxtend.frequent_patterns import apriori
from mlxtend.frequent_patterns import association_rules


Have a look here.