Reference · Statistics

stats.mean

stats.mean(list) -> float

What it does

The arithmetic mean of a list of numbers, as a float: the sum divided by the count. It is Python's statistics.mean: the sum is compensated and rounded once, so the answer is the correctly rounded mean, not the running sum divided by the count. Ints and floats can mix; the answer is always a float.

Examples

print(stats.mean([1, 2, 3, 4]), stats.mean([1, 2, 3]), stats.mean([1.5, 2.5]))print(stats.mean([0.1, 0.2, 0.3]), (0.1 + 0.2 + 0.3) / 3)

2.5 2.0 2.00.2 0.20000000000000004

Edge cases

• One number gives that number, as a float.
• The list is read into floats first: an int past 2⁵³ (about 9 × 10¹⁵) loses its last digits, where Python keeps them exact.
• Floats whose sum overflows the largest float still give their mean, as in Python.
• Decimals are not accepted; convert them with float.

print(stats.mean([5]), stats.mean([-1, 1]))print(stats.mean([9007199254740993, 9007199254740993]))print(stats.mean([1e308, 1e308]))print(stats.mean([dec("1.5"), dec("2.5")] | map(float)))

5.0 0.09007199254740992.01000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000002.0

Errors

The mean of no data is a runtime error, not an error value: an empty list or null stops the request. So does a list with something that is not a number in it, or a decimal. Guard with len when the list can be empty.

print(stats.mean([]))

line 1: `stats.mean` requires at least one data point

print(stats.mean([1, "2"]))

line 1: `stats.mean` expects a list of numbers, but item 1 is str

let xs = []print(len(xs) > 0 && stats.mean(xs))

false

Chaining

In a pipeline the list comes first, so stats.mean takes it as its only argument:

print([3, 1, 2] | stats.mean)print([{ total: 12.5 }, { total: 30 }, { total: 7.5 }] | vec.column("total") | stats.mean)print([1, 2, 3, 10] | filter((x) => x < 10) | stats.mean)

2.016.6666666666666682.0

See also

stats.fmean, stats.median, stats.geometric_mean, stats.harmonic_mean, stats.describe, sum, math.fsum

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