#!/usr/bin/python


def outlierCleaner(predictions, ages, net_worths):
    """
        Clean away the 10% of points that have the largest
        residual errors (difference between the prediction
        and the actual net worth).

        Return a list of tuples named cleaned_data where 
        each tuple is of the form (age, net_worth, error).
    """
    
    cleaned_data = range(len(ages))
    for i in range(len(predictions)):
        error = predictions[i] - net_worths[i]
        error = error*error
        cleaned_data[i] = (ages[i], net_worths[i], error)
        

    cleaned_data.sort(key=lambda tup: tup[2])
    
    return cleaned_data[:int(len(cleaned_data)*.9)]