python3 ks检验 求平均值方差标准差中位数 dataframe使用engine写入数据库 pandas使用

需求是这样的：将两个数据集进行ks检验，算中位数方差标准差等数据，最后输出到数据库中
import psycopg2
import os
import pandas as pd
from scipy.stats import ks_2samp
import numpy as np
from sqlalchemy import create_engine

# 用来操作数据库的类
class GPCommand(object):
    # 类的初始化
    def __init__(self):
      self.hostname = 'ip'
      self.username = 'name'
      self.password = 'password'
      self.database = 'database'
    def connectGp(self):
      try:
        #链接数据库
        #读取配置利用connect链接数据库
        self.connect = psycopg2.connect( host=self.hostname, user=self.username, password=self.password, dbname=self.database )
        #创建一个新的cursor
        self.cursor = self.connect.cursor()
        print("connect gp successful."+'\n' + '数据库连接成功')
        return ('con_successful')
      except psycopg2.Error:
          error = 'Failed to setup Postgres environment.\n{0}'.format(sys.exc_info())
          print('connect gp error.'+'\n' + '数据库连接失败')
          return 'con_error'+ error
#关闭数据库
    def closeMysql(self):
        self.cursor.close()
        self.connect.close()
        print("数据库已关闭")

    def select_data(self):
        try:
            select_sql1 = "select distinct a.order_no,a.cust_no,b.item_num from dw_edw.edw_ord_fct_order_info a " \
                         "left join (select order_no,count(distinct item_no) as item_num " \
                         "from dw_edw.edw_ord_fct_order_items_info group by order_no)b on a.order_no=b.order_no where a.customer_confirm_date>='2015-01-01 00:00:00'  "
            self.cursor.execute(select_sql1)
            rows1 = self.cursor.fetchall()

            select_sql2 = "select t1.*,t2.order_num,t3.item_num from( select distinct cust_no,order_no from dw_edw.edw_ord_fct_order_info " \
                          "where customer_confirm_date>='2015-01-01 00:00:00' )t1 left join ( select cust_no,count(distinct order_no) as order_num " \
                          "from dw_edw.edw_ord_fct_order_info where customer_confirm_date>='2015-01-01 00:00:00' group by cust_no )t2 on t1.cust_no=t2.cust_no " \
                          "left join  (select order_no,count(distinct item_no) as item_num from dw_edw.edw_ord_fct_order_items_info group by order_no)t3 " \
                          "on t1.order_no=t3.order_no where t2.order_num>5   "
            self.cursor.execute(select_sql2)
            rows2 = self.cursor.fetchall()
            return rows1,rows2
        except Exception as e:
            print(e)
            os._exit(0)

def kt(df1,df2,x):
    itemks = df1['item_num1']
    beta = itemks

    result2 =df2.groupby('cust_no2').count().reset_index()
    res_fram2 = result2.iloc[x]
    res_fram2_cust = res_fram2['cust_no2']
    res2 = df2[df2['cust_no2'] == res_fram2_cust]
    sampleks = res2['item_num2']
    norm = sampleks
    ks = ks_2samp(beta, norm)
    # df = pd.DataFrame({'cust_no':[res_fram2_cust],
    #                 'pvalue': [ks.pvalue]})

    return res_fram2_cust,ks.pvalue


def insert_data(data):
    engine = create_engine('postgresql://name:password@ip:port/database')
    try:
        data.to_sql('market_sales_precision_ks_p_test',schema='dw_ana', con=engine, index=False, if_exists='append')
    except Exception as e:
        print(e)


def main():
    gpCommand = GPCommand()
    gpCommand.connectGp()
    ####计算平均值和方差/标准差
    res1 = gpCommand.select_data()
    cust_no1 = []
    item_num1 = []
    for item1 in res1[0]:
        cust_no1.append(item1[1])
        item_num1.append(item1[2])
    df1 = pd.DataFrame({'cust_no1':cust_no1,
                       'item_num1':item_num1 })
    result1 = df1.groupby('cust_no1')
    res1_mean1 = df1.groupby('cust_no1').mean()
    res1_var1 = df1.groupby('cust_no1').var()
    res1_std1 = df1.groupby('cust_no1').std()
    res1_median1 = df1.groupby('cust_no1').median()

    cust_no2 = []
    item_num2 = []
    for item2 in res1[1]:
        cust_no2.append(item2[0])
        item_num2.append(item2[3])
    df2 = pd.DataFrame({'cust_no2': cust_no2,
                       'item_num2': item_num2})
    res1_mean2 = df2.groupby('cust_no2').mean()
    res1_var2 = df2.groupby('cust_no2').var()
    res1_std2 = df2.groupby('cust_no2').std()
    res1_median2 = df2.groupby('cust_no2').median()

####分布分析-ks检验
    data = []
    df2_size = df2.groupby('cust_no2').size()
    for i in range(0,len(df2_size)):
        datef = kt(df1,df2,i)
        data.append(datef)
    data_cust1=[]
    data_p1=[]
    for item in data:
        data_cust1.append(item[0])
        data_p1.append(item[1])
    df_pdata = pd.DataFrame({'cust_no': data_cust1,
                       'p': data_p1})
    gpCommand.closeMysql()
    insert_data(df_pdata)




main()