如何计算散点图中每个簇的距离 plot

Question

I have 2 clusters plotted in a scatter plot and i need to find their standard deviation and distance from the center from one cluster to another. I was not able to find any guide of documentation that simplifies the process of finding the center of 2 clusters for scatter plots, the reason is that i need to compare the scatter of each cluster with the distance of the centres of the clusters. My actual scatter plot looks like this:

import matplotlib.pyplot as plt
import numpy as np


vector1 = [
    2.8238, 
    3.0284, 
    5.9333,
    2.0156,
    2.2467, 
    2.0092,
    4.7983, 
    4.3554,
    3.6372, 
    1.3159, 
    2.6174, 
    2.2336, 
    0.9625, 
    5.6285, 
    5.4040, 
    2.7887, 
    0, 
    3.4632, 
    0, 
    2.7370
]  
vector5 = [
    1.2994, 
    7.4469,
    3.6503, 
    2.1667,
    4.1975, 
    3.3006, 
    10.4082, 
    3.4112, 
    2.2395, 
    1.5653, 
    4.3237, 
    1.8679, 
    1.2622, 
    14.1372, 
    6.1686, 
    3.8903, 
    2.2873, 
    6.2559, 
    0.2132, 
    7.2303,
]

plt.rcParams['figure.figsize'] = (16.0, 10.0)
plt.style.use('ggplot')
data = [vector1, std_colomns4]

plt.plot(vector1 , marker='.', linestyle='none', markersize=20, label='Vector 1')
plt.plot(vector5, marker='.', linestyle='none', markersize=20, label='Vector 5')


plt.xticks(range(1, 20, 1))
plt.yticks(range(1, 20, 1))
plt.ylabel('Sizes')
plt.xlabel('Index')
plt.legend()
plt.show()

For the sake of pre-visualization:

Answer 1

You can compute the mean by converting them to arrays

vector1 = np.array([...])
vector5 = np.array([...])

mean1 = np.mean(vector1)
mean5 = np.mean(vector5)

# Rest of the code

plt.plot((vector1+vector5)/2, marker='x', linestyle='none', markersize=12, label='Mean')

plt.axhline(mean1)
plt.axhline(mean5, c='b')