import numpy as np
import pandas as pd

data={}

ma={}
   
da = pd.read_csv('tude.csv')  #longitude latitude in cities
pin=pd.read_csv('PINGAN-2018-train_demo.csv')
pi=pd.DataFrame(pin,columns=['TERMINALNO','LONGITUDE','LATITUDE','Y'])
ping=pi[pi.Y>0]

for person in ping.TERMINALNO:
    
   
    data[person]=pd.DataFrame(ping[ping.TERMINALNO==person ],columns=['TERMINALNO','LONGITUDE','LATITUDE','Y'])
    ma[person]={'ao':max(data[person].LONGITUDE),'io':min(data[person].LONGITUDE),'aa':max(data[person].LATITUDE),'ia':min(data[person].LATITUDE),'y':max(data[person].Y)}


##ao :max longitude     io:min longitude     aa:  max latitude       ia:  min latitude   y  rate in csv
inf=pd.DataFrame(columns=['person','city','y'])
for person in ma:    
   
    for i in dat.city:
        if (ma[person]['io']<=dat.lon[i-1] <=ma[person]['ao'] )& (ma[person]['ia']<=dat.lat[i-1]<=ma[person]['aa']):
            
            inf.loc[inf.shape[0]+1] = {'person':person,'city':i,'y':ma[person]['y']}
#            inf=inf.append(l,ignore_index=True)