Haversine Distance Calc using Pandas Data Frame "cannot convert the series to <class 'float'>"

Question

Im trying to use the Haversine calc on a Panda Dataframe.

from math import radians, cos, sin, asin, sqrt
    
def haversine(lon1, lat1, lon2, lat2):
        
        # convert decimal degrees to radians 
        lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])
    
        # haversine formula 
        dlon = lon2 - lon1 
        dlat = lat2 - lat1 
        a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
        c = 2 * asin(sqrt(a)) 
       
        r = 3956
        
        return c * r

This works when using the following code:

haversine(-73.9881286621093,40.7320289611816,-73.9901733398437,40.7566795349121)

However, when I use it against a Pandas DataFrame as such:

train_data['Distance_Travelled'] = train_data.apply(lambda row: haversine(train_data['pickup_longitude'], train_data['pickup_latitude'], train_data['dropoff_longitude'], train_data['dropoff_latitude']), axis=1)

I get the following error.

"cannot convert the series to <class 'float'>"

I've tried numerous ways of casting but each attempt results in the same error. I know that math is expecting float, but I don't understand why the Pandas series can't be cast as a float.

What edit needs to be made for it to work and why?

Answer 1

Don't use apply since it is not vectorized. Also, use the vectorized functions from numpy:

def haversine(lon1, lat1, lon2, lat2):
    lon1, lat1, lon2, lat2 = np.deg2rad([lon1, lat1, lon2, lat2])

    dlon = lon2 - lon1 
    dlat = lat2 - lat1 
    a = np.sin(dlat/2)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2)**2
    c = 2 * np.asin(np.sqrt(a)) 

    r = 3956

    return c * r

train_data['Distance_Travelled'] = haversine(train_data['pickup_longitude'], 
                                             train_data['pickup_latitude'], 
                                             train_data['dropoff_longitude'], 
                                             train_data['dropoff_latitude']
                                            )