Tukey method outlier detection

outlier_detection

import numpy as np
from collections import Counter


def detect_outliers(df, n, features):
    """
    Takes a dataframe df of features and returns a list of the indices
    corresponding to the observations containing more than n outliers according
    to the Tukey method.
    """
    outlier_indices = []

    # iterate over features(columns)
    for col in features:
        # 1st quartile (25%)
        Q1 = np.percentile(df[col], 25)
        # 3rd quartile (75%)
        Q3 = np.percentile(df[col], 75)
        # Interquartile range (IQR)
        IQR = Q3 - Q1

        # outlier step
        outlier_step = 1.5 * IQR

        # Determine a list of indices of outliers for feature col
        outlier_list_col = df[(df[col] < Q1 - outlier_step) | (df[col] > Q3 + outlier_step)].index

        # append the found outlier indices for col to the list of outlier indices 
        outlier_indices.extend(outlier_list_col)

    # select observations containing more than 2 outliers
    outlier_indices = Counter(outlier_indices)        
    multiple_outliers = list(k for k, v in outlier_indices.items() if v > n)

    return multiple_outliers

# detect outliers from list of features
lof = ['example_feature_1', 'example_feature_2']
# params dataset, number of outliers for rejection, list of features
Outliers_to_drop = detect_outliers(dataset, 2, lof)

https://www.itl.nist.gov/div898/handbook/prc/section4/prc471.htm

The idea is that things that are outliers generally trend together. For example, if you have a set of weights and heights what would height and weight trend together?

This is basically a naive attempt at outlier classification without using ML.