common_price

# -*- coding: utf-8 -*-"""Created on Wed Sep 20 10:51:47 2017@author: Administrator"""import pymongofrom pymongo import MongoClientimport numpy as npimport pandas as  pdfrom pandas import DataFrame,Seriesfrom numpy import row_stack,column_stackfrom dateutil.parser import parsefrom matplotlib.pylab import date2numimport randomdef commonprice(ct,rl):    client1 = MongoClient('192.168.0.136',27017)    db1 = client1.fangjia    seaweed1 = db1.seaweed    query1 = {"status":0,"cat":"district","city":ct,"region":rl}    fields1 = {"lat2":1,"lng2":1, "city":1,"region":1,"cat":1,"name":1}    lct= list()    for s in seaweed1.find(query1, fields1):        lct.append(s)    lf=DataFrame(lct)    print(lf)    lk=list(lf['name'])    #从公司的数据库中导入数据    client = MongoClient('192.168.10.88',27777)    db = client.fangjia    seawater = db.seawater    seawater.find_one()    import random    from collections import Counter        for i in range(len(lk)):        query = {"city":ct,"cat":"sell","region":rl,                 "district_name":lk[i],"p_date":{"$gt":20170701}}        lt= seawater.count(query)        #print(lt)        pos = list()        for s in seawater.find(query).limit(lt):            pos.append(s)        data=DataFrame(pos)        if data.shape[0]>3:            ppd=data[['total_price','area','district_name']]            ppd1=ppd.dropna()            avv=ppd1['total_price']/ppd1['area']            #suma=ppd1['area'].values.sum()            avv1=list(avv)            avv2=(np.array(avv1)//2000)*2000            #avp=int(avv.mean())            mostn=Counter(avv2).most_common(1)            mm=mostn[0][0]            nn=mostn[0][1]            print(mm,ppd1['district_name'][1],nn,rl)            client1 = MongoClient('192.168.0.136',27017)            db1 = client1.fangjia_stat            stat = db1.district_stat            stat.save({"city":ct,"region":rl,"district_name":ppd1['district_name'][1],               "avg_price":mm, "weeedend":"2017-09-22","acuracy":nn})cti=["上海"]region_list=["黄浦","徐汇","长宁","虹口","静安","普陀",             "杨浦","闵行","宝山","嘉定","浦东新","金山","松江",              "青浦","奉贤"]    for i in region_list:    commonprice(cti[0],i)