Raymond Hettinger <[email protected]> added the comment:
>> def fmean(seq: Sequence[float]) -> float:
>> return math.fsum(seq) / len(seq)
>
> Is it intentional that this doesn't support iterators?
Since we need both the sum and the length, this seemed like a good starting
point. Also, the existing mean() function already covers the more general
cases.
I suspect that it is common to keep the data in memory so that more than one
descriptive statistic can be generated:
data = load_measurements()
data.sort()
n = len(data)
mu = fastmean(data)
sigma = stdev(data, xbar=mu)
low, q1, q2, q3, high = data[0], data[n//4], data[n//2], data[3*n//4],
data[-1]
popular = mode(data, first_tie=True)
It's possible (though possibly not desirable) to provide an fallback path:
def fastmean(data: Iterable) -> float:
try:
return fsum(data) / len(data)
except TypeError:
# Slow alternative
return float(mean(data))
# Memory intensive alternative
data = list(data)
return fsum(data) / len(data)
# Less accurate alternative
total = n = 0
for n, x in enumerate(data, start=1):
total += x
return total / n
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<https://bugs.python.org/issue35904>
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