Efficient Decision Tree Pruning

Is there an efficient way to handle pruning in Decision Tree with Python ?

Currently I’m doing that:

def do_best_tree(Xtrain, ytrain, Xtest, ytest):
    clf = DecisionTreeClassifier()
    clf.fit(Xtrain, ytrain)
    path = clf.cost_complexity_pruning_path(Xtrain, ytrain)
    ccp_alphas = path.ccp_alphas
    clfs = []
    for ccp_alpha in tqdm(ccp_alphas):
        clf = DecisionTreeClassifier(ccp_alpha=ccp_alpha)
        clf.fit(Xtrain, ytrain)
        clfs.append(clf)
    return max(clfs, key=lambda x:x.score(Xtest, ytest))

But it’s super slow (as I create and fit a lot of trees).

Is there a more efficient way to do this with scikit-learn, or another library that handle this ?