Reference · Statistics

stats.quantiles

stats.quantiles(list, int=, str=) -> list

What it does

The cut points that divide a list of numbers into n parts of equal probability, as Python's statistics.quantiles: n - 1 floats in rising order. n is 4 by default (quartiles); 10 gives deciles, 100 percentiles. The method is "exclusive" by default, which treats the data as a sample and places the cut points as if the smallest and largest values were not quite the extremes; "inclusive" treats the data as the whole population, so the first and last cut points cannot go past the data. The list need not be sorted.

Examples

print(stats.quantiles([1, 2, 3, 4, 5]))print(stats.quantiles([1, 2, 3, 4, 5], 4, "inclusive"))print(stats.quantiles([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 10))

[1.5, 3.0, 4.5][2.0, 3.0, 4.0][1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 8.8, 9.9]

Edge cases

• n can be from 1 to 10,000. n = 1 gives []: no cut points.
• One data point gives that point n - 1 times.
• Exclusive cut points can lie outside the data: [1, 2] gives 0.75 and 2.25. Inclusive ones never do.
• The answer is floats, also for int data, and the method can be null for the default.
• A whole-number float for n, such as 4.0, is accepted as the int.

print(stats.quantiles([1, 2, 3], 1), stats.quantiles([5]))print(stats.quantiles([1, 2], 4), stats.quantiles([1, 2], 4, "inclusive"))print(stats.quantiles([3, 1, 2], 2, null), stats.quantiles([3, 1, 2], 2.0))

[] [5.0, 5.0, 5.0][0.75, 1.5, 2.25] [1.25, 1.5, 1.75][2.0] [2.0]

Errors

No data, an n outside 1 to 10,000, a method other than "exclusive" or "inclusive", or an item that is not a number are each a runtime error, not an error value.

print(stats.quantiles([1, 2, 3], 0))

line 1: `stats.quantiles`: n must be from 1 to 10000, got 0

print(stats.quantiles([1, 2, 3], 4, "nearest"))

line 1: `stats.quantiles`: unknown method "nearest" (exclusive or inclusive)

print(stats.quantiles([]))

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

Chaining

let data = [105, 129, 87, 86, 111, 111, 89, 81, 108, 92, 110, 100, 75, 105, 103, 109, 76, 119, 99, 91]print(data | stats.quantiles)print(data | stats.quantiles(10) | map((q) => round(q, 1)))let [q1, q2, q3] = data | stats.quantiles(4, "inclusive")print(q3 - q1)

[87.5, 101.5, 109.75][76.5, 86.2, 89.6, 94.8, 101.5, 105.0, 108.7, 110.8, 118.2]20.75

See also

stats.percentile, stats.median, stats.describe, sort

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