缺失值处理

import pandas as pdaimport numpy as npyimport matplotlib.pylab as pyl# data=pda.read_excel("D:/taobao2.xls")def index(data):  data = pda.DataFrame(data[1:],columns=data[0])  print(data)  data["价格"][(data["价格"]==0)]=None  print(data)  x=0  for i in data.columns:   for j in range(len(data)):     if(data[i].isnull())[j]:        data[i][j]=data["价格"].mean()        x+=1        print(x)  if __name__ == "__main__":  data = nosupervision_read_data()  index(data)

数据离散化处理

#离散化#连续型数据离散化#等宽离散化import pandas as pdaimport numpy as npyimport matplotlib.pylab as pyl# data=pda.read_excel("D:/taobao2.xls")def index(data):    data = pda.DataFrame(data[1:], columns=data[0])    da=data.values    price=da[:,2]    price.sort()    print(price)    k=5    c1=pda.cut(price,k,labels=["太便宜","便宜","适中","贵","太贵"])    print(c1)#指点区间离散化    k=[0,50,100,price.max()]    print(k)    c2=pda.cut(price,k,labels=["非常便宜","适中","贵"])    print(c2)if __name__ == "__main__":   data = nosupervision_read_data()   index(data)

数据集成处理

# -*- coding:utf-8 -*-# 异常值处理import pandas as pdaimport numpy as npydef index(data):# 输出结果必须为字典output   output = {}# data = pda.read_excel("D:/taobao2.xls")   data = pda.DataFrame(data[1:], columns=data[0])# print(data)   da = data.values# 数据集成   da1 = da[0:10]   da2 = da[10:20]   da3 = npy.concatenate((da1, da2))   pda.DataFrame(da3)   output['data_数据集成'] = pda.DataFrame(da3).values.tolist()   print(pda.DataFrame(da1))   print(pda.DataFrame(da2))   print(pda.DataFrame(da3))   print(output)   return outputif __name__ == "__main__":   data = nosupervision_read_data()   index(data)