预处理算法_12_分组聚合

 

#!/usr/bin/env python# -*- coding:utf-8 -*-# <editable>def execute():# <editable>'''载入模块'''import pandas as pdfrom sqlalchemy import create_engine'''连接数据库'''engine = create_engine('mysql+pymysql://root:123123qwe@127.0.0.1:3306/analysis')'''选择目标数据'''params = {"features": "score as fea","label": 'score',"method": "count",}inputs = {"table": 'test'}sql = 'select ' + params['features'] + ',' + params['label'] + ' from ' + inputs['table']data_in = pd.read_sql_query(sql, engine)data_in = data_in.fillna(float(20))print(data_in)'''分组聚合用法：obj.groupby(‘key’)obj.groupby([‘key1’,’key2’])'''b = params['label'].split(',')if params['method'] == 'count':data_out = data_in.groupby(b).count().reset_index()elif params['method'] == 'max':data_out = data_in.groupby(b).max().reset_index()elif params['method'] == 'mean':data_out = data_in.groupby(b).mean().reset_index()elif params['method'] == 'median':data_out = data_in.groupby(b).median().reset_index()elif params['method'] == 'size':data_out = data_in.groupby(b).size().reset_index()elif params['method'] == 'min':data_out = data_in.groupby(b).min().reset_index()elif params['method'] == 'std':data_out = data_in.groupby(b).std().reset_index()else:data_out = data_in.groupby(b).sum().reset_index()'''将结果写出'''print(data_out)'''数据示例fea  score0  80.080.01  20.020.02  20.020.035.05.044.04.05  20.020.0score  fea04.0115.01220.03380.01'''# </editable>if __name__ == '__main__':execute()