python - Why is statistics.mean() so slow?

Question

I compared the performance of the mean function of the statistics module with the simple sum(l)/len(l) method and found the mean function to be very slow for some reason. I used timeit with the two code snippets below to compare them, does anyone know what causes the massive difference in execution speed? I'm using Python 3.5.

from timeit import repeat
print(min(repeat('mean(l)',
                 '''from random import randint; from statistics import mean; 
                 l=[randint(0, 10000) for i in range(10000)]''', repeat=20, number=10)))

The code above executes in about 0.043 seconds on my machine.

from timeit import repeat
print(min(repeat('sum(l)/len(l)',
                 '''from random import randint; from statistics import mean; 
                 l=[randint(0, 10000) for i in range(10000)]''', repeat=20, number=10)))

The code above executes in about 0.000565 seconds on my machine.

Answer 1

Python's statistics module is not built for speed, but for precision

In the specs for this module, it appears that

The built-in sum can lose accuracy when dealing with floats of wildly differing magnitude. Consequently, the above naive mean fails this "torture test"

assert mean([1e30, 1, 3, -1e30]) == 1

returning 0 instead of 1, a purely computational error of 100%.

Using math.fsum inside mean will make it more accurate with float data, but it also has the side-effect of converting any arguments to float even when unnecessary. E.g. we should expect the mean of a list of Fractions to be a Fraction, not a float.

Conversely, if we take a look at the implementation of _sum() in this module, the first lines of the method's docstring seem to confirm that:

def _sum(data, start=0):
    """_sum(data [, start]) -> (type, sum, count)

    Return a high-precision sum of the given numeric data as a fraction,
    together with the type to be converted to and the count of items.

    [...] """

So yeah, statistics implementation of sum, instead of being a simple one-liner call to Python's built-in sum() function, takes about 20 lines by itself with a nested for loop in its body.

This happens because statistics._sum chooses to guarantee the maximum precision for all types of number it could encounter (even if they widely differ from one another), instead of simply emphasizing speed.

Hence, it appears normal that the built-in sum proves a hundred times faster. The cost of it being a much lower precision in you happen to call it with exotic numbers.

Other options

If you need to prioritize speed in your algorithms, you should have a look at Numpy instead, the algorithms of which being implemented in C.

NumPy mean is not as precise as statistics by a long shot but it implements (since 2013) a routine based on pairwise summation which is better than a naive sum/len (more info in the link).

However...

import numpy as np
import statistics

np_mean = np.mean([1e30, 1, 3, -1e30])
statistics_mean = statistics.mean([1e30, 1, 3, -1e30])

print('NumPy mean: {}'.format(np_mean))
print('Statistics mean: {}'.format(statistics_mean))

> NumPy mean: 0.0
> Statistics mean: 1.0