I will write about numerical summarization, which is the basic summarization method for data analysis.
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)
Please refer to here for the meaning of each word. https://note.com/karaage_love/n/n6f617d38c528
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.///
See here for a detailed summary of 2D data. https://note.com/karaage_love/n/n992a7fdf9b1f