For my CS class I the task given was to take a given Person Class with a name and age attributes.

Then, with an existing list of Person Objects, create a function that returns a tuple of (mean, median, mode) on the given age attribute.

Simple enough, but I wondered if my solution is really all that efficient. I basically iterated a new list of ages from an existing list of Person objects, and that almost feels like there's a better opportunity to optimize a solution...

Wondering if I'm missing something obvious.

from statistics import mean, median, mode

class Person:

    def __init__(self, name, age):
        initializes a person objects name and age
        self._name = name
        self._age = age

    def get_age(self):
        returns the private data member _age
        return self._age

def basic_stats(person_list):
    creates a list of ages from a list of person objects then calculates
    mean, median, and mode from the statistics module
    age_list = [i.get_age() for i in person_list]

    return (mean(age_list), median(age_list), mode(age_list))
  • 1
    \$\begingroup\$ The iteration is unavoidable and list generators are pretty cheap. Overall, the get_age() attribute seems quite roundabout but OOP is pretty poorly taught so I suspect they're enforcing it and you just have to go with it \$\endgroup\$ Commented Jan 3, 2021 at 22:47

2 Answers 2


In terms of performance, to create a list of ages, [i.get_age() for i in person_list] is about as good as you'll get. The only real way to avoid this is to adjust the mean, median, and mode functions to accept a key parameter (much like the built-in sort function does) so that each function will access the correct attribute of the object. I wouldn't worry about removing that list comprehension though. Unless person_list is huge, and you require this code to run often, and as fast as possible, that would be a premature optimization.

Another way to potentially speed it up would be to have a single function that iterates the list once, and calculates all three statistics in one go. Again though, I would consider that to be premature unless you had a very good reason to optimize that.

Your solution is fine until you run into a situation in which it isn't performing well. Favor readability and simplicity until performance becomes an actual concern (or you have good reason to believe that it will become an actual concern in the near future).


For efficiency's sake, without breaking the parameters of your assignment, the only other thing you can add is __slots__.

Beyond that - in the "real world" - if this were a truly massive list, you would want to do away with the class representation and instead use a Numpy matrix, for which the calculation of summary statistics will be faster due to vectorization.


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