import  numpy as np

x=np.array([1,2,3,4.5,5,6.5,7,8,9,10])

average=np.mean(x)  ///Mean value mean function///
(Out  5.6)

med=np.median(x)   ///Median function///
(Out  5.75)

var.p=np.var(x)  ///Sample variance var function///
(Out  8.19)

std=np.std(x)   ///Standard deviation std function///
(Out  2.86)

import numpy as np
import matplotlib.pyplot as plt

array=np.loadtxt(fname='example.csv',delimiter=',',encoding="utf-8_sig")
///example.csv contains two columns of data.///

array_x=array[:,0]
array_y=array[:,1]  ///slice///

plt.scatter(araay_x,array_y,s=10,c='blue',alpha='0.5')  

///Creating a scatter plot s is the size c is the color of the scatter plot alpha is the transparency///

np.cov(array_x,array_y,bias=True) 
(Out   [[6.72727273 3.54545455]
        [3.54545455 6.        ]])
 //The covariance result is a 2 × 2 matrix. The diagonal components are the variances of x and y, respectively. The rest is covariance.///
np.corrcoef(array_x,array_y)  
(Out   [[1.         0.55805471]
        [0.55805471 1.        ]]
///Correlation coefficient: After all, the correlation coefficient is other than the diagonal component.///

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