运行结果: 程序代码如下: #将excel中的数据进行读取分析import openpyxlimport numpy as npimport mathimport matplotlib.pyplot as pitwk=openpyxl.load_workbook('信息11.xlsx')sheet=wk.activerows=sheet.max_rowcols=sheet.ma
运行结果:
程序代码如下:
#将excel中的数据进行读取分析 import openpyxl import numpy as np import math import matplotlib.pyplot as pit wk=openpyxl.load_workbook('信息11.xlsx') sheet=wk.active rows=sheet.max_row cols=sheet.max_column lst1=[] lst2=[] for i in range (1,rows+1): size1=sheet.cell(i,1).value lst1.append(size1) size2 = sheet.cell(i, 2).value lst2.append(size2) num=0 dic_size={} for item in lst1: dic_size[lst1[num]]=lst2[num] num+=1 #弄成百分比的形式 lst_total=[] for item in dic_size: lst_total.append([item,dic_size[item]]) labels=[item[0] for item in lst_total] #使用列表生成式,得到饼图的标签 fraces=[item[1] for item in lst_total] #饼图中的数据源 pit.rcParams['font.family']=['SimHei'] #单独的表格乱码的处理方式 pit.scatter(labels,fraces) pit.plot(labels,fraces,color='green') pit.bar(labels,fraces,width=5,color='red') z1=np.polyfit(labels,fraces,2) p1=np.poly1d(z1) x = np.linspace(0, 500, 50) y=-0.00024*(x**2)+0.1013*(x)+10.23 pit.plot(x,y,color='purple') #pit.savefig('图.jpg') yre=[] for item in labels: y=-0.00024*(item**2)+0.1013*(item)+10.23 yre.append(round(y,6)) print(fraces) print(yre) result=[] a=0 mse=0 mae=0 for i in range(0,10): a+=round(fraces[i]-yre[i],6) mae+=round(math.fabs(fraces[i]-yre[i]),6) for i in range(0,10): result.append(round(fraces[i] - yre[i]-round(a/10,6), 6)) mse += round((fraces[i] - yre[i]-round(a/10,6)) * (fraces[i] - yre[i]-round(a/10,6)), 6) print(result) print('均值',round(a/10,6)) print('均方误差',round(mse/10,6)) rmse=math.sqrt(round(mse/10,6)) print('均方根误差',round(rmse,6)) print('平均绝对误差',round(mae/10,6)) print('R平方的数值',1-((round(a/10,6))*round(a/10,6))/round(mse/10,6)) print(p1) #pit.show()
以上就是本文的全部内容,希望对大家的学习有所帮助,也希望大家多多支持易盾网络。