The code in the statistics library is actually quite a bit more complicated than I had expected. Is there a reason why a function like `mean` isnt just: `sum(data) / len(data)`?
if iter(data) is data:
data = list(data)
n = len(data)
if n < 1:
raise StatisticsError('mean requires at least one data point')
T, total, count = _sum(data)
assert count == n
return _convert(total / n, T)
In general though, a literal translation of a formula is not always great in terms of numerical stability/error. For example, you might have learned to calculate the variance as mean(X^2) - mean(X)^2, but this can lead to a huge loss of precision that more complicated approaches avoid.
> if iter(data) is data:
Wouldn't it be cheaper like `if type(data) is iter:`?
And why convert `data` to a list at all, to check for length 0, given that `_sum(data)` will return the count?