outlier_std.py

import numpy as np
import matplotlib.pyplot as plt
seed(1)


# multiply and add by random numbers to get some real values
data = np.random.randn(50000)  * 20 + 20

# Function to Detection Outlier on one-dimentional datasets.
def find_anomalies(data):
    #define a list to accumlate anomalies
    anomalies = []
    
    # Set upper and lower limit to 3 standard deviation
    random_data_std = std(random_data)
    random_data_mean = mean(random_data)
    anomaly_cut_off = random_data_std * 3
    
    lower_limit  = random_data_mean - anomaly_cut_off 
    upper_limit = random_data_mean + anomaly_cut_off
    print(lower_limit)
    # Generate outliers
    for outlier in random_data:
        if outlier > upper_limit or outlier < lower_limit:
            anomalies.append(outlier)
    return anomalies

find_anomalies(data)

'random_data' in the definition of the function should be replaced by 'data'