sumeyye_outlier.py

import pandas as pd
import numpy as np

d = {'col1': [1, 2, 3, 4, 5, 6, 100], 'col2': [  2,   4,   6,   8,  10,  12, 200]}
df = pd.DataFrame(d)
print(df)


def detect_outlier(df, threshold):
    outliers = []
    means = []
    stds = []
    
    for i in range(0,len(df.columns)):
        means.append(np.mean(df.iloc[:,i]))
        stds.append(np.std(df.iloc[:,i]))
        
        for df_value in df.iloc[:,i]:
            z_score = (df_value - means[i]) / stds[i]
            
            if np.abs(z_score) > threshold:
                outliers.append(df_value)
                
    return outliers, means, stds

print(detect_outlier(df, threshold = 2))

Thank you Mr. Cobanov !