# def split_train_test(data, ratio):
#     total_len = len(data)
#     shuffled = np.random.permutation(total_len) #randomiza
#     shuffled = np.random.seed(42) #randomiza
#     size = int(total_len * ratio)
#     test_indexes = shuffled[:size]
#     train_indexes = shuffled[size:]
#     return data.iloc[train_indexes], data.iloc[test_indexes] #

# sys.exit()

# examples[examples.ca.apply(lambda x: x.isnumeric())]


# clone[attribute] = clone[attribute].fillna((clone[attribute].mean()))


# for index, row in data.iterrows():