Incrementally update geocoded data

#!/usr/bin/python 
# -*- coding: utf-8 -*-

import requests
import sqlite3
import json
import pandas as pd 

conn = sqlite3.connect('marathon.sqlite')
cur = conn.cursor()

# do we need to create the t_geocode table?
cur.execute("SELECT count(1) from sqlite_master WHERE type='table' AND name='t_geocode' ") 
n=cur.fetchone()[0]
if n==0:
    cur.execute('''
            CREATE TABLE t_geocode(
                gemeente varchar(128),
                ioc varchar(128),
                lat double,
                lon double
                )''') 

# read the file with mapping from 3 digit country code to country name into a dataframe
df=pd.io.parsers.read_table('/u01/data/20150215_country_code_iso/country_codes.txt',sep='|')

# turn the dataframe into a dictionary
c3d=df.set_index('c3')['country'].to_dict()

# select the records to geocode 
cur.execute(''' SELECT distinct r.gemeente,r.ioc,g.gemeente, g.ioc
                FROM   t_result r 
                       left outer join t_geocode g
                                   on (r.gemeente=g.gemeente and r.ioc=g.ioc)
                WHERE g.gemeente is null ''') 

n=50 # batch size: change this to your liking. Small in the beginning (look at exceptional cases etc..) 
for row in cur.fetchall(): 
    (g,c)=row[:2] # ignore the latter 2 fields
    print "---", g,c, "----------------------------------"
    if g=='X' or len(g)==0 or g=='Gemeente':
        print "skip"
        continue

    cy=c3d[c]
    print "{} -> {}".format(c,cy) 
    
    url=u'http://nominatim.openstreetmap.org/search?country={}&city={}&format=json'.format( cy,g ) 
    r = requests.get(url)
    jr=json.loads(r.text)
    (lat,lon)=(0.0,0.0)
    if len(jr)>0:
        lat=jr[0]['lat']
        lon=jr[0]['lon']
    print "{} {} {} -> ({},{})".format(g,c,cy,lat,lon)
    cur.execute('''insert into t_geocode( gemeente , ioc , lat, lon)
                       values(?,?,?,?)''', ( g,c,lat,lon) ) 
    # batch
    n-=1
    if n==0:
        break

cur.close()
conn.commit()