Two very simple functions to estimate the number of outliers according to the 3-sigma rule and quantile rule

outliers.py

import numpy as np
import pandas as pd


def get_quantile_outliers(series):
    outliers_dic = {}

    outliers_list = []

    iqr = series.quantile(0.75) - series.quantile(0.25)
    lower_bound = series.quantile(0.25) - (1.5 * iqr)
    upper_bound = series.quantile(0.75) + (1.5 * iqr)
    outliers_dic['lower_bound'] = lower_bound
    outliers_dic['upper_bound'] = upper_bound

    for i in series:
        if i >= upper_bound or i <= lower_bound:
            outliers_list.append(i)

    outliers_dic['number_of_outliers'] = len(outliers_list)
    outliers_dic['outliers_list'] = np.array(outliers_list)

    return outliers_dic


def get_3sigma_outliers(series):
    outliers_dic = {}

    sigma = series[((series - series.mean()).abs() > 3 * series.std())]
    outliers_list = sigma.values
    outliers_dic['outliers_list'] = outliers_list
    outliers_dic['number_of_outliers'] = len(outliers_list)
    return outliers_dic