Why is statistics.mean() so slow?

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

if you do care of speed use numpy/scipy/pandas instead:

In [119]: from random import randint; from statistics import mean; import numpy as np;

In [122]: l=[randint(0, 10000) for i in range(10**6)]

In [123]: mean(l)
Out[123]: 5001.992355

In [124]: %timeit mean(l)
1 loop, best of 3: 2.01 s per loop

In [125]: a = np.array(l)

In [126]: np.mean(a)
Out[126]: 5001.9923550000003

In [127]: %timeit np.mean(a)
100 loops, best of 3: 2.87 ms per loop

Conclusion: it will be orders of magnitude faster - in my example it was 700 times faster, but maybe not that precise (as numpy doesn't use Kahan summation algorithm).

I asked the same question a while back but once I noticed the _sum function called in mean on line 317 in the source I understood why:

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.
    If optional argument ``start`` is given, it is added to the total.
    If ``data`` is empty, ``start`` (defaulting to 0) is returned.
    Examples
    --------
    >>> _sum([3, 2.25, 4.5, -0.5, 1.0], 0.75)
    (<class 'float'>, Fraction(11, 1), 5)
    Some sources of round-off error will be avoided:
    >>> _sum([1e50, 1, -1e50] * 1000)  # Built-in sum returns zero.
    (<class 'float'>, Fraction(1000, 1), 3000)
    Fractions and Decimals are also supported:
    >>> from fractions import Fraction as F
    >>> _sum([F(2, 3), F(7, 5), F(1, 4), F(5, 6)])
    (<class 'fractions.Fraction'>, Fraction(63, 20), 4)
    >>> from decimal import Decimal as D
    >>> data = [D("0.1375"), D("0.2108"), D("0.3061"), D("0.0419")]
    >>> _sum(data)
    (<class 'decimal.Decimal'>, Fraction(6963, 10000), 4)
    Mixed types are currently treated as an error, except that int is
    allowed.
    """
    count = 0
    n, d = _exact_ratio(start)
    partials = {d: n}
    partials_get = partials.get
    T = _coerce(int, type(start))
    for typ, values in groupby(data, type):
        T = _coerce(T, typ)  # or raise TypeError
        for n,d in map(_exact_ratio, values):
            count += 1
            partials[d] = partials_get(d, 0) + n
    if None in partials:
        # The sum will be a NAN or INF. We can ignore all the finite
        # partials, and just look at this special one.
        total = partials[None]
        assert not _isfinite(total)
    else:
        # Sum all the partial sums using builtin sum.
        # FIXME is this faster if we sum them in order of the denominator?
        total = sum(Fraction(n, d) for d, n in sorted(partials.items()))
    return (T, total, count)

There is a multitude of operations happening in comparison to just calling the builtin sum, as per the doc strings mean calculates a high-precision sum.

You can see using mean vs sum can give you different output:

In [7]: l = [.1, .12312, 2.112, .12131]

In [8]: sum(l) / len(l)
Out[8]: 0.6141074999999999

In [9]: mean(l)
Out[9]: 0.6141075