Python 反向地理编码(无谷歌API);邮政编码
将美国地区的地理代码“纬度”和“经度”反译为邮政编码的代码;最初用于确定纽约枪击事件的邮政编码。示例输出:Python 反向地理编码(无谷歌API);邮政编码,python,pandas,reverse-geocoding,zipcode,Python,Pandas,Reverse Geocoding,Zipcode,将美国地区的地理代码“纬度”和“经度”反译为邮政编码的代码;最初用于确定纽约枪击事件的邮政编码。示例输出: lat lon zipcode 0 40.896504 -73.859042 10470 1 40.732804 -74.005666 10014 2 40.674142 -73.936206 11213 3 40.648025 -73.904011 11236 4 40.764694 -73.9143
lat lon zipcode
0 40.896504 -73.859042 10470
1 40.732804 -74.005666 10014
2 40.674142 -73.936206 11213
3 40.648025 -73.904011 11236
4 40.764694 -73.914348 11103
... ... ... ...
20654 40.710989 -73.942949 11211
20655 40.682398 -73.840079 11416
20656 40.651014 -73.945707 11226
20657 40.835990 -73.916276 10452
20658 40.857771 -73.894606 10458
加载数据集(非必需):
反向地理编码代码:
pip install uszipcode
# Import packages
from uszipcode import SearchEngine
search = SearchEngine(simple_zipcode=True)
from uszipcode import Zipcode
import numpy as np
#define zipcode search function
def get_zipcode(lat, lon):
result = search.by_coordinates(lat = lat, lng = lon, returns = 1)
return result[0].zipcode
#load columns from dataframe
lat = df_shooting['Latitude']
lon = df_shooting['Longitude']
#define latitude/longitude for function
df = pd.DataFrame({'lat':lat, 'lon':lon})
#add new column with generated zip-code
df['zipcode'] = df.apply(lambda x: get_zipcode(x.lat,x.lon), axis=1)
#print result
print(df)
#(optional) save as csv
#df.to_csv(r'zip_codes.csv')
注意长时间运行(20k行=5-7分钟)。然而,我们在不利用(付费)谷歌API的情况下设法找出了最有效的代码。这里是另一个解决方案(包括注释代码):
pip install uszipcode
# Import packages
from uszipcode import SearchEngine
search = SearchEngine(simple_zipcode=True)
from uszipcode import Zipcode
import numpy as np
#define zipcode search function
def get_zipcode(lat, lon):
result = search.by_coordinates(lat = lat, lng = lon, returns = 1)
return result[0].zipcode
#load columns from dataframe
lat = df_shooting['Latitude']
lon = df_shooting['Longitude']
#define latitude/longitude for function
df = pd.DataFrame({'lat':lat, 'lon':lon})
#add new column with generated zip-code
df['zipcode'] = df.apply(lambda x: get_zipcode(x.lat,x.lon), axis=1)
#print result
print(df)
#(optional) save as csv
#df.to_csv(r'zip_codes.csv')