Visualizing association rules in Python code example

Example: visualizing association rule with python

dataset = [['Milk', 'Onion', 'Nutmeg', 'Kidney Beans', 'Eggs', 'Yogurt'],
           ['Dill', 'Onion', 'Nutmeg', 'Kidney Beans', 'Eggs', 'Yogurt'],
           ['Milk', 'Apple', 'Kidney Beans', 'Eggs'],
           ['Milk', 'Unicorn', 'Corn', 'Kidney Beans', 'Yogurt'],
           ['Corn', 'Onion', 'Onion', 'Kidney Beans', 'Ice cream', 'Eggs']]
            
            
import pandas as pd
from mlxtend.preprocessing import OnehotTransactions
from mlxtend.frequent_patterns import apriori
 
oht = OnehotTransactions()
oht_ary = oht.fit(dataset).transform(dataset)
df = pd.DataFrame(oht_ary, columns=oht.columns_)
print (df)           
 
frequent_itemsets = apriori(df, min_support=0.6, use_colnames=True)
print (frequent_itemsets)
 
association_rules(frequent_itemsets, metric="confidence", min_threshold=0.7)
rules = association_rules(frequent_itemsets, metric="lift", min_threshold=1.2)
print (rules)
 
"""
Below is the output
    support                     itemsets
0       0.8                       [Eggs]
1       1.0               [Kidney Beans]
2       0.6                       [Milk]
3       0.6                      [Onion]
4       0.6                     [Yogurt]
5       0.8         [Eggs, Kidney Beans]
6       0.6                [Eggs, Onion]
7       0.6         [Kidney Beans, Milk]
8       0.6        [Kidney Beans, Onion]
9       0.6       [Kidney Beans, Yogurt]
10      0.6  [Eggs, Kidney Beans, Onion]
 
             antecedants            consequents  support  confidence  lift
0  (Kidney Beans, Onion)                 (Eggs)      0.6        1.00  1.25
1   (Kidney Beans, Eggs)                (Onion)      0.8        0.75  1.25
2                (Onion)   (Kidney Beans, Eggs)      0.6        1.00  1.25
3                 (Eggs)  (Kidney Beans, Onion)      0.8        0.75  1.25
4                (Onion)                 (Eggs)      0.6        1.00  1.25
5                 (Eggs)                (Onion)      0.8        0.75  1.25
 
"""