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This article will explain in detail about python correlation analysis and drawing scattered point example analysis, Xiaobian think it is quite practical, so share it for everyone to make a reference, I hope you can gain something after reading this article.

Library needed

The main libraries used are pandas, numpy, matplotlib, seaborn, etc. If you want to use seaborn library, you must introduce matplotlib library, seaborn is its plug-in library.

#1 load pakeage import pandas as pd#read and write tables and table processing import numpy as np#for data computation import geopandas as gpdimport matplotlib.pyplot as pltimport seaborn as snsimport osimport warningswarnings.filterwarnings ("ignore")plt.rc ('font',family='Times New Roman')from glob import globfrom osgeo import gdal,gdal_array, gdalnumericplt.rcParams <$'font.sans-serif']=<$'Simhei'] #Display Chinese plt.rcParams <$'axes.unicode_minus']=False #Display sign data read

Here I am using a portion of my junior brother's data, reading the data here.

df = pd.read_csv('./ JXDY_1.csv',encoding='utf-8')df.head()

Here we do correlation analysis

df.corr()#default is pearson correlation analysis

Then we do a batch scatter plot output, putting correlation data on the graph

a = 2#where the correlation is calculated from the second bit, so I extract for i in df.columns[3:]: a1 = sns.lmplot(y='fruit',x=i,data=df) ax = plt.gca() ax.text(0.9,1,"Pearson:{:.2f}".format(df.corr().iloc[1,a]),transform=ax.transAxes)#Add correlation a = a+1 plt.savefig('./ {}.jpg'.format(i),dpi=300,bbox_inches = 'tight')

The result is done, and then the result output can be!

About "python correlation analysis and drawing scattered point example analysis" This article is shared here, I hope the above content can be of some help to everyone, so that you can learn more knowledge, if you think the article is good, please share it for more people to see.

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