sgmean for Python

Proportional trimmed mean compatible with Statgraphics.

Resolves the integer truncation problem of scipy.stats.trim_mean.

Installation

pip install sgmean

The problem

scipy.stats.trim_mean computes the trimmed mean via integer truncation: it calculates k = proportiontocut * n and silently reduces it to the nearest integer below. This generates two types of silent distortion:

• Type A (k < 1): returns the arithmetic mean without any trimming
• Type B (k > 1, non-integer): trims fewer observations than requested

The solution

sgmean applies a fractional discount (1 - delta) to boundary observations, where delta = k - floor(k):

from sgmean import sgmean
import numpy as np

x = np.array([850, 920, 980, 1050, 1120, 1180, 1250,
              1320, 1400, 1480, 1550, 1700, 1850, 2100, 8500])

# Type A distortion (k = 0.05 * 15 = 0.75)
from scipy import stats
stats.trim_mean(x, 0.05)  # 1816.667 — no trimming applied
sgmean(x, trim=0.05)      # 1499.074 — correct proportional discount

# Type B distortion (k = 0.10 * 15 = 1.50)
stats.trim_mean(x, 0.10)  # 1376.923 — 33% trimming loss
sgmean(x, trim=0.10)      # 1365.833 — matches Statgraphics exactly

Reference

Gaviria-Chaverra, J.C. (2026). sgmean: A Proportional Trimmed Mean for R Compatible with Statgraphics. The R Journal. DOI: 10.32614/CRAN.package.sgmean

License

MIT