# [PYTHON] Numerical summary of data

I will write about numerical summarization, which is the basic summarization method for data analysis.

# Summary of one-dimensional data

``````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

# Summary of 2D data

``````import numpy as np
import matplotlib.pyplot as plt

///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