Reverse Geocoding For Pandas Dataframe

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?

Solution 1:

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