Filtering redundant Association rules

After performing association rule mining, (using mlxtend library), I have gotten
a pandas dataframe containing all the association rules. This contains too many rules,
therefore we must reduce the number.

For an association rule $R: X implies Y$

For any $Z subset X$

If any rule $R’: X – Z implies Y$ exists, such that $$confidence(R) pm alpha = confidence(R’)$$

Then remove $R$

I tried to implement that logic directly into python as such

def filter_useless(rules):
    shadow = rules
    def remove_one(tup):
        x , y, conf= tup['antecedents'] , tup['consequents'], tup['confidence']
        for s in powerset(x):
            res = rules[  (rules['antecedents'] == s) 
                        & (rules['consequents'] == y) 
                        & (abs(rules['confidence'] - conf) <= 0.1)]
            if not res.empty:
                return False
        return True
    return shadow[shadow.apply(remove_one, axis=1)]

However, this didn’t filter any rules. What am I doing wrong?