How to extract sklearn decision tree rules to pandas boolean conditions?

There are so many posts like this about how to extract sklearn decision tree rules but I could not find any about using pandas.

Take this data and model for example, as below

# Create Decision Tree classifer object
clf = DecisionTreeClassifier(criterion="entropy", max_depth=3)

# Train Decision Tree Classifer
clf = clf.fit(X_train,y_train)

The result:

Expected:

There're 8 rules about this example.

From left to right,notice that dataframe is df

r1 = (df['glucose']<=127.5) & (df['bmi']<=26.45) & (df['bmi']<=9.1)
……
r8 =  (df['glucose']>127.5) & (df['bmi']>28.15) & (df['glucose']>158.5)

I'm not a master of extracting sklearn decision tree rules. Getting the pandas boolean conditions will help me calculate samples and other metrics for each rule. So I want to extract each rule to a pandas boolean condition.

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