ori_filename = "data/geo_code_repl_cut_9.xlsx"
colnames = ['门店名称','地址','合并地址','id','name','type','typecode','address','location','pcode','pname','citycode','cityname','adcode','adname','business_area','timestamp','rating','cost']
colnames += ['车辆服务', '餐饮服务', '生活服务', '体育服务', '娱乐服务', '医疗服务', '住宿服务', '商住区', '风景区', '教育院校', '交通枢纽', '公共交通', '购物专卖','购物综合']
loc_df = fr.readAddressFile(ori_filename, colnames)
poly_list = list(loc_df['location'])
ct_list = ["洛阳市"] * len(poly_list)
field = '政府机构社会团体'
types = '130000'
extractor.typefilter = []
top = -1
extractor.infoExtract(poly_list, ct_list, types, top, simplify = True)
new_filename = "data/geo_code_repl_cut_9.xlsx"
loc_df[field] = extractor.result
fr.writeAddressFile(loc_df, new_filename)
#################################
############# 科教文化 ##############
colnames = ['门店名称','地址','合并地址','id','name','type','typecode','address','location','pcode','pname','citycode','cityname','adcode','adname','business_area','timestamp','rating','cost']
colnames += ['车辆服务', '餐饮服务', '生活服务', '体育服务', '娱乐服务', '医疗服务', '住宿服务', '商住区', '风景区', '教育院校', '交通枢纽', '公共交通', '购物专卖', '购物综合', '政府机构社会团体']
loc_df = fr.readAddressFile(ori_filename, colnames)
poly_list = list(loc_df['location'])
ct_list = ["洛阳市"] * len(poly_list)
field = '科教文化'
types = '140000'
extractor.typefilter = ['141201','141206']
top = -1
'061101',\
'061102',\
'061103',\
'061104',\
'061201',\
'061202',\
'061203',\
'061204',\
'061205',\
'061206',\
'061207',\
'061208',\
'061209',\
'061211',\
'061212',\
'061213',\
'061214',\
'061300',\
'061301',\
'061302'
]
extractor.infoExtract(poly_list, ct_list, types, top)
import file_reader as fr
field=['id', 'name', 'type', 'typecode', 'address', 'location', 'pcode', 'pname', 'citycode', \
'cityname', 'adcode', 'adname', 'business_area', 'timestamp', 'rating', 'cost']
structured_data = fr.genStructuredData(extractor.result, field)
new_filename = 'data/geo_code_replenish_basic.xlsx'
fr.writeAddressFile(structured_data, new_filename)
def main():
ori_filename = "data/combined.xlsx"
colnames = ["门店名称", "地址"]
addr_df = fr.readAddressFile(ori_filename, colnames)
addr_df["合并地址"] = addr_df.apply(lambda x: x["地址"] + x["门店名称"], axis=1)
#addr_df = addr_df.loc[:30,:]
############# Using Geo Coder API #############
api_url = "https://restapi.amap.com/v3/geocode/geo"
api_key = "Classified. You can obtain one on AMap Website as an Enterprise."
field = [
'formatted_address', 'country', 'province', 'citycode', 'city',
'district', 'adcode', 'location', 'level'
]
geocoder = ge.GeoInfoExtractor(api_url, api_key)
#addr_list = addr_df["合并地址"]
#city_list = ["洛阳"]
#extractor.infoExtract(addr_list, city_list, True)
#extractor.poiAmend(list(addr_df["门店名称"]), city_list)
############# =================== #############
############# Using POI Search API #############
api_url = "https://restapi.amap.com/v3/place/text"
api_key = "Classified. You can obtain one on AMap Website as an Enterprise."
field=['id', 'name', 'type', 'typecode', 'address', 'location', 'pcode', 'pname', 'citycode', \
'cityname', 'adcode', 'adname', 'business_area', 'timestamp', 'rating', 'cost']
extractor = ps.POISearcher(api_url, api_key)
extractor.typefilter = ['060301',\
'060302',\
'060303',\
'060304',\
'060305',\
'060306',\
'060307',\
'060308',\
'060500',\
'060501',\
'060502',\
'060600',\
'060601',\
'060602',\
'060603',\
'060604',\
'060605',\
'060606',\
'060701',\
'060702',\
'060705',\
'060706',\
'060800',\
'060900',\
'060901',\
'060902',\
'060903',\
'060904',\
'060905',\
'060906',\
'060907',\
'061000',\
'061001',\
'061100',\
'061101',\
'061102',\
'061103',\
'061104',\
'061201',\
'061202',\
'061203',\
'061204',\
'061205',\
'061206',\
'061207',\
'061208',\
'061209',\
'061211',\
'061212',\
'061213',\
'061214',\
'061300',\
'061301',\
'061302'
]
remove_digits = str.maketrans('', '', digits)
kw_list = list(
addr_df["门店名称"]) #.apply(lambda x:x.translate(remove_digits))
ct_list = ["洛阳"] * len(kw_list)
types = "060000"
top = 1
extractor.infoExtract(kw_list, ct_list, types, top, geocoder,
list(addr_df["合并地址"])) #地址
############# =================== #############
structured_data = fr.genStructuredData(extractor.result, field)
for f in field:
addr_df[f] = list(structured_data[f])
new_filename = 'data/geo_code_basic.xlsx'
fr.writeAddressFile(addr_df, new_filename)