Is there a numpy builtin to reject outliers from a list

Question

Is there a numpy builtin to do something like the following? That is, take a list d and return a list filtered_d with any outlying elements removed

Answer 1

Consider that all the above methods fail when your standard deviation gets very large due to huge outliers.

(Simalar as the average caluclation fails and should rather caluclate the median. Though, the average is "more prone to such an error as the stdDv".)

You could try to iteratively apply your algorithm or you filter using the interquartile range: (here "factor" relates to a n*sigma range, yet only when your data follows a Gaussian distribution)

import numpy as np

def sortoutOutliers(dataIn,factor):
    quant3, quant1 = np.percentile(dataIn, [75 ,25])
    iqr = quant3 - quant1
    iqrSigma = iqr/1.34896
    medData = np.median(dataIn)
    dataOut = [ x for x in dataIn if ( (x > medData - factor* iqrSigma) and (x < medData + factor* iqrSigma) ) ] 
    return(dataOut)