new column with coordinates using geopy pandas

Question

I have a df:

import pandas as pd
import numpy as np
import datetime as DT
import hmac
from geopy.geocoders import Nominatim
from geopy.distance import vincenty

df


     city_name  state_name  county_name
0    WASHINGTON  DC  DIST OF COLUMBIA
1    WASHINGTON  DC  DIST OF COLUMBIA
2    WASHINGTON  DC  DIST OF COLUMBIA
3    WASHINGTON  DC  DIST OF COLUMBIA
4    WASHINGTON  DC  DIST OF COLUMBIA
5    WASHINGTON  DC  DIST OF COLUMBIA
6    WASHINGTON  DC  DIST OF COLUMBIA
7    WASHINGTON  DC  DIST OF COLUMBIA
8    WASHINGTON  DC  DIST OF COLUMBIA
9    WASHINGTON  DC  DIST OF COLUMBIA

I want to get the latitude and longitude coordinates for any one of the columns in the data frame below. The documentation (http://geopy.readthedocs.org/en/latest/#data) is pretty straightforward when working with the documentation for individual locations.

>>> from geopy.geocoders import Nominatim
>>> geolocator = Nominatim()
>>> location = geolocator.geocode("175 5th Avenue NYC")
>>> print(location.address)
Flatiron Building, 175, 5th Avenue, Flatiron, New York, NYC, New York,     ...
>>> print((location.latitude, location.longitude))
(40.7410861, -73.9896297241625)
>>> print(location.raw)
{'place_id': '9167009604', 'type': 'attraction', ...}

However I want to apply the function to each row in the df and make a new column. I've tried the following

df['city_coord'] = geolocator.geocode(lambda row: 'state_name' (row))

but I think I'm missing something in my code because I get the following:

    city_name   state_name  county_name coordinates
0    WASHINGTON  DC  DIST OF COLUMBIA    None
1    WASHINGTON  DC  DIST OF COLUMBIA    None
2    WASHINGTON  DC  DIST OF COLUMBIA    None
3    WASHINGTON  DC  DIST OF COLUMBIA    None
4    WASHINGTON  DC  DIST OF COLUMBIA    None
5    WASHINGTON  DC  DIST OF COLUMBIA    None
6    WASHINGTON  DC  DIST OF COLUMBIA    None
7    WASHINGTON  DC  DIST OF COLUMBIA    None
8    WASHINGTON  DC  DIST OF COLUMBIA    None
9    WASHINGTON  DC  DIST OF COLUMBIA    None

I would like something like this hopefully using the Lambda function:

     city_name  state_name  county_name  city_coord
0    WASHINGTON  DC  DIST OF COLUMBIA    38.8949549, -77.0366456 
1    WASHINGTON  DC  DIST OF COLUMBIA    38.8949549, -77.0366456 
2    WASHINGTON  DC  DIST OF COLUMBIA    38.8949549, -77.0366456 
3    WASHINGTON  DC  DIST OF COLUMBIA    38.8949549, -77.0366456 
4    WASHINGTON  DC  DIST OF COLUMBIA    38.8949549, -77.0366456 
5    WASHINGTON  DC  DIST OF COLUMBIA    38.8949549, -77.0366456 
6    WASHINGTON  DC  DIST OF COLUMBIA    38.8949549, -77.0366456 
7    WASHINGTON  DC  DIST OF COLUMBIA    38.8949549, -77.0366456 
8    WASHINGTON  DC  DIST OF COLUMBIA    38.8949549, -77.0366456 
9    WASHINGTON  DC  DIST OF COLUMBIA    38.8949549, -77.0366456
10   GLYNCO      GA  GLYNN               31.2224512, -81.5101023

I appreciate any help. After I get the coordinates I'd like to map them. Any recommended resources for mapping coordinates is greatly appreciated too. thanks

Answer 1

You can call apply and pass the function you want to execute on every row like the following:

In [9]:

geolocator = Nominatim()
df['city_coord'] = df['state_name'].apply(geolocator.geocode)
df
Out[9]:
    city_name state_name       county_name  \
0  WASHINGTON         DC  DIST OF COLUMBIA   
1  WASHINGTON         DC  DIST OF COLUMBIA   

                                          city_coord  
0  (District of Columbia, United States of Americ...  
1  (District of Columbia, United States of Americ...  

You can then access the latitude and longitude attributes:

In [16]:

df['city_coord'] = df['city_coord'].apply(lambda x: (x.latitude, x.longitude))
df
Out[16]:
    city_name state_name       county_name                       city_coord
0  WASHINGTON         DC  DIST OF COLUMBIA  (38.8937154, -76.9877934586326)
1  WASHINGTON         DC  DIST OF COLUMBIA  (38.8937154, -76.9877934586326)

Or do it in a one liner by calling apply twice:

In [17]:
df['city_coord'] = df['state_name'].apply(geolocator.geocode).apply(lambda x: (x.latitude, x.longitude))
df

Out[17]:
    city_name state_name       county_name                       city_coord
0  WASHINGTON         DC  DIST OF COLUMBIA  (38.8937154, -76.9877934586326)
1  WASHINGTON         DC  DIST OF COLUMBIA  (38.8937154, -76.9877934586326)

Also your attempt geolocator.geocode(lambda row: 'state_name' (row)) did nothing hence why you have a column full of None values

EDIT

@leb makes an interesting point here, if you have many duplicate values then it'll be more performant to geocode for each unique value and then add this:

In [38]:
states = df['state_name'].unique()
d = dict(zip(states, pd.Series(states).apply(geolocator.geocode).apply(lambda x: (x.latitude, x.longitude))))
d

Out[38]:
{'DC': (38.8937154, -76.9877934586326)}

In [40]:    
df['city_coord'] = df['state_name'].map(d)
df

Out[40]:
    city_name state_name       county_name                       city_coord
0  WASHINGTON         DC  DIST OF COLUMBIA  (38.8937154, -76.9877934586326)
1  WASHINGTON         DC  DIST OF COLUMBIA  (38.8937154, -76.9877934586326)

So the above gets all the unique values using unique, constructs a dict from them and then calls map to perform the lookup and add the coords, this will be more efficient than trying to geocode row-wise