Detecting outliers from a list

Question

I want to detect and store outliers from a list and this is what I am doing

Code:

def outliers(y,thresh=3.5):
    
    m = np.median(y)
    abs_dev = np.abs(y - m)
    left_mad = np.median(abs_dev[y <= m])
    right_mad = np.median(abs_dev[y >= m])
    y_mad = left_mad * np.ones(len(y))
    y_mad[y > m] = right_mad
    modified_z_score = 0.6745 * abs_dev / y_mad
    modified_z_score[y == m] = 0
    return modified_z_score > thresh

bids = [5000,5500,4500,1000,15000,5200,4900]

z = outliers(bids)
bidd = np.array(bids)
out_liers = bidd[z]

This gives results as:

out_liers = array([ 1000, 15000])

Is there a better way to do this, where I don't get the results in array but in a list? Also please can someone explain me why we used

thresh=3.5

modified_z_score = 0.6745 * abs_dev / y_mad

Answer 1

This works:

def outliers_modified_z_score(ys, threshold=3.5):
    ys_arr = np.array(ys)
    median_y = np.median(ys_arr)
    median_absolute_deviation_y = np.median(np.abs(ys_arr - median_y))
    modified_z_scores = 0.6745 * (ys_arr - median_y) / median_absolute_deviation_y
    return (ys_arr[np.abs(modified_z_scores) > threshold]).tolist()

Answer 2

That's because you are using numpy function. Default type used there is numpy.ndarray, which speeds up the computations. In the case you just need a list as output argument, use tolist() method.

z = outliers(bids)
bidd = np.array(bids)
out_liers = bidd[z].tolist()