How do I calculate the euclidean distance to the nearest neighbour for each coordinates pair in meters in Pandas dataframe?

Question

I have a dataframe like this

index	place id	var_lat_fact	var_lon_fact
0	167312091448	5.6679820000	-0.0144950000
1	167312091448	5.6686320000	-0.0157910000
2	167312091448	5.6653530000	-0.0181980000
3	167312091448	5.6700970000	-0.0191400000
4	167312091448	5.6689810000	-0.0104040000

For each coordinates pair (lat, lon) I'd like to calculate the euclidean distance to the nearest neighbour within the dataframe. So each point gets a metric in the additional column (say, nearest_neighbour_dist) indicating that distance in meters.

Something like this

index	place id	var_lat_fact	var_lon_fact	nearest_neighbour_dist
0	167312091448	5.6679820000	-0.0144950000	160.588370
1	167312091448	5.6686320000	-0.0157910000	160.588370
2	167312091448	5.6653530000	-0.0181980000	451.525301
3	167312091448	5.6700970000	-0.0191400000	404.794908
4	167312091448	5.6689810000	-0.0104040000	466.104453

Just can't get my head around this... Any help would be greatly appreciated.

Answer 1

You can use sklearn's NearestNeighbors:

from sklearn.neighbors import NearestNeighbors
from numpy import deg2rad

# set up the nearest neighbors
neigh = NearestNeighbors(n_neighbors=1, metric='haversine')
data = deg2rad(df[['var_lat_fact', 'var_lon_fact']])
neigh.fit(data)

# find the closest two points
# the closest distance is self, the second one is the closest non-self
df['nearest_neighbour_dist'] = (neigh.kneighbors(data,
                                                 n_neighbors=2, return_distance=True
                                                )[0][:, -1]
                                *6371*1000
                               )

Output:

   index      place_id  var_lat_fact  var_lon_fact  nearest_neighbour_dist
0      0  167312091448      5.667982     -0.014495              160.588370
1      1  167312091448      5.668632     -0.015791              160.588370
2      2  167312091448      5.665353     -0.018198              451.525301
3      3  167312091448      5.670097     -0.019140              404.794908
4      4  167312091448      5.668981     -0.010404              466.104453

Points on a map

I wanted to double check the validity of the computations

1 -> 2 (index 0-> 1 in your data) is indeed about 160.6 meters