Reference · Statistics

stats.normalize

stats.normalize(list | null) -> list

What it does

Each number of a list scaled to 0..1: the smallest value becomes 0.0, the largest 1.0, and the rest keep their proportions between them. The answer is a list of floats in the same order. It is min-max scaling, not in Python's statistics module; for scaling by the spread use stats.zscore.

Examples

print(stats.normalize([1, 2, 3, 4, 5]))print(stats.normalize([72, 85, 90, 66, 85, 78]) | map((v) => round(v, 2)))

[0.0, 0.25, 0.5, 0.75, 1.0][0.25, 0.79, 1.0, 0.0, 0.79, 0.5]

Edge cases

• A constant list gives zeros, not an error, and so does a list of one.
• An empty list, or null, gives [].
• Negative numbers are fine; the smallest is the zero point.
• The order is kept: a falling list gives a falling answer.

print(stats.normalize([5, 5, 5]), stats.normalize([3]), stats.normalize([]))print(stats.normalize([-2, 0, 2]), stats.normalize([10, 0]))

[0.0, 0.0, 0.0] [0.0] [][0.0, 0.5, 1.0] [1.0, 0.0]

Errors

An item that is not a number is a runtime error, not an error value. So is an argument that is not a list or null.

print(stats.normalize([1, null]))

line 1: `stats.normalize` expects a list of numbers, but item 1 is null

Chaining

print([1, 2, 3] | stats.normalize | map((v) => v * 100))print([4, 1, 3] | stats.normalize | map((v) => round(v, 2)))

[0.0, 50.0, 100.0][1.0, 0.0, 0.67]

See also

stats.zscore, stats.rank, min, max, vec.clip

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