How to find the closest match based on 2 keys from one dataframe to another?

Question

I have 2 dataframes I\'m working with. One has a bunch of locations and coordinates (longitude, latitude). The other is a weather data set with data from weather stations al

Answer 1

Let's say you have a distance function dist that you want to minimize:

def dist(lat1, long1, lat2, long2):
    return np.abs((lat1-lat2)+(long1-long2))

For a given location, you can find the nearest station as follows:

lat = 39.463744
long = -76.119411
weather.apply(
    lambda row: dist(lat, long, row['Latitude'], row['Longitude']), 
    axis=1)

This will calculate the distance to all weather stations. Using idxmin you can find the closest station name:

distances = weather.apply(
    lambda row: dist(lat, long, row['Latitude'], row['Longitude']), 
    axis=1)
weather.loc[distances.idxmin(), 'StationName']

Let's put all this in a function:

def find_station(lat, long):
    distances = weather.apply(
        lambda row: dist(lat, long, row['Latitude'], row['Longitude']), 
        axis=1)
    return weather.loc[distances.idxmin(), 'StationName']

You can now get all the nearest stations by applying it to the locations dataframe:

locations.apply(
    lambda row: find_station(row['Latitude'], row['Longitude']), 
    axis=1)

Output:

0         WALTHAM
1         WALTHAM
2    PORTST.LUCIE
3         WALTHAM
4    PORTST.LUCIE