Robust Estimation of Mean and Standard Deviation in Python via the Huber Estimator

huber.py

import numpy as np
from statsmodels.robust.scale import huber

# Mean and standard deviation to generate normal random variates
mean, std_dev = 0, 2
sample_size = 25
np.random.seed(42)
x = np.random.normal(mean, std_dev, sample_size)

# Appends a couple of outliers
x = np.append(x, (50, 100))

# Notice that the Huber estimators are much closer
# to the *true* mean and standard deviation.
print (np.mean(x), np.std(x)) # (5.253, 20.946)
print huber(x) # (array(-0.033), array(2.367))