Pulse Documentation

name: op-agg-skewness
description: Population skewness g1 (m3 / m2^1.5, dividing by n) via online moments.
kind: operator
category: AGG
operator: AGG_SKEWNESS
type: reference
applies_to: process, compose, predict
examples_tags: [distribution-shape, streaming-friendly]

Use when

How lopsided a numeric field is: positive when a long tail runs to high values, negative when it runs to low ones.

Questions it answers:

• Are incomes in each region bunched low with a few very high ones?
• Is the delivery-time distribution symmetric or dragged out by slow orders?

Use something else:

• AGG_KURTOSIS when you want to know whether the tails are heavy rather than lopsided.

Params

Weight-aware ("weight": null opts out): weighted population moments, Σw in place of n. Invalid weights excluded (PULSE_WEIGHT_INVALID_ROWS).

Inputs

Output

Scalar float64 — population skewness g1 = m3 / m2^1.5 (moments divide by n), NOT the adjusted G1 of Excel SKEW / SPSS. Per-group when wired under a grouper.

Reading the output

• value: How lopsided the values are around their mean. Pulse computes the population moment coefficient g1: the average cubed z-score, (m3/n) / (m2/n)^1.5, where the components m2 and m3 are SUMS of squared and cubed distances from the mean (not yet divided by n). 0 means the values balance around the mean.
  • Sign: positive means a longer or heavier tail to the right: a few values sit far above the rest; negative means a longer or heavier tail to the left: a few values sit far below the rest.

Components

Weighted adds floor sum_weights, n_eff (probability), n_weight_invalid.

Universal floor {n, n_null} plus operator-specific:

• Mergeability: Mergeable (Chan moments combine)
• Streaming: per-chunk online moments

Gotchas

• n <= 1 or zero variance → 0 (not NaN): check n before reading a 0.
• Small-n bias: G1 = g1·√(n(n−1))/(n−2), so |g1| < |G1| on small groups.
• decimal128 rejected — cast via ATTR_FORMULA.
• Sensitive to outliers; pre-filter or use rank-based alternatives.

See

• pulse_examples_search tags=[distribution-shape]
• Skills: aggregation-design, response-components