Reference · Statistics

stats.percentile

stats.percentile(list, num | list) -> any

What it does

The value below which p percent of a list of numbers lies, with p from 0 to 100, by numpy's default rule: the sorted data is laid out from 0 to 100 and the answer is interpolated linearly between the two closest data points. It is not in Python's statistics module; stats.quantiles is the Python way. With one p the answer is a float; with a list of them, a list of floats in the same order. The list need not be sorted.

Examples

print(stats.percentile([1, 2, 3, 4, 5], 50))print(stats.percentile([1, 2, 3, 4, 5], 90))print(stats.percentile([1, 2, 3, 4, 5], [0, 25, 100]))

3.04.6[1.0, 2.0, 5.0]

Edge cases

• 0 gives the smallest value and 100 the largest; 50 is the median.
• The answer is a float even for int data and whole-number results.
• One data point gives that point for any p.
• p can be a float, and an empty list of p gives [].
• Between two points the answer is linear: [10, 20] at 50 is 15.0.

print(stats.percentile([3, 1, 2], 0), stats.percentile([3, 1, 2], 100))print(stats.percentile([5, 1, 3], 50) == stats.median([5, 1, 3]))print(stats.percentile([7], 30), stats.percentile([10, 20], 50), stats.percentile([1, 2, 3, 4, 5], [10.5]))print(stats.percentile([1, 2, 3], []))

1.0 3.0true7.0 15.0 [1.42][]

Errors

A p outside 0 to 100, in a list too, is a runtime error, not an error value. So are no data, a missing p, and a p that is not a number.

print(stats.percentile([1, 2, 3], 101))

line 1: `stats.percentile`: percentiles are from 0 to 100, got 101

print(stats.percentile([1, 2, 3], [50, -1]))

line 1: `stats.percentile`: percentiles are from 0 to 100, got -1

print(stats.percentile([], 50))

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

Chaining

In a pipeline the data comes first and p is the argument:

let latency = [12, 15, 11, 240, 14, 13, 18, 16, 15, 12]print(latency | stats.percentile(95))print(latency | stats.percentile([50, 90, 99]) | map((v) => round(v, 1)))

140.09999999999977[14.5, 40.2, 220.0]

See also

stats.quantiles, stats.median, stats.describe, stats.rank

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