pandas DataFrame的反向地理编码 [英] Reverse geocoding for pandas DataFrame

问题描述

我们有经度和纬度数据，需要将它们转换为纽约的邮政编码。
是否可以处理20,000行的python包？

We have longitude and latitude data and need to transform them into zip codes for new york city. Is there any way to do with a python package for 20,000 rows?

推荐答案

uszipcode包可以完成您的工作

The uszipcode package can do what you're looking for.

from uszipcode import SearchEngine
search = SearchEngine(simple_zipcode=True)
from uszipcode import Zipcode
import numpy as np

def get_zipcode(lat, lon):
    result = search.by_coordinates(lat = lat, lng = lon, returns = 1)
    return result[0].zipcode

lat = np.random.uniform(35,45,10)
lon = np.random.uniform(-100, -110, 10)
df = pd.DataFrame({'lat':lat, 'lon':lon})

df['zipcode'] = df.apply(lambda x: get_zipcode(x.lat,x.lon), axis=1)
df

    lat          lon        zipcode
0   35.535132   -104.418912 88421
1   39.949551   -108.999900 81648
2   39.684619   -104.583286 80018
3   42.080516   -104.489692 82243
4   39.944844   -101.249686 67745
5   38.437412   -101.276961 67861
6   38.900596   -105.557827 80820
7   36.879532   -106.541044 87520
8   43.241656   -107.312935 82630
9   41.541356   -103.589179 69345

这篇关于 pandas DataFrame的反向地理编码的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！