Is there a way to calculate nearest lat_longs and then club them together in python?

What I want to do is to calculate and club those lat_longs into a single lat_long that have a haversine distance smaller than 1km and push them into a list and also those lat_longs that do not have distance smaller than 1km.

I have used haversine to calculate haversine distance distance.

def get_dist(loc_1,loc_2):
    
    loc_1 = loc_1.split(",")
    loc_2 = loc_2.split(",")
    
    loc_1 = (float(loc_1[0]),float(loc_1[1]))
    loc_2 = (float(loc_2[0]),float(loc_2[1]))
    
    val = hs.haversine(loc_1,loc_2)
    
    return val

So basically my aim was to cluster the geo spatial locations to find out Natural-Gas pumps in the database.

I used DBSCAN for this.

Code:-

    final_df[['latitude','longitude']] = 
    final_df['start_cord'].str.split(",",expand=True)
    print(len(final_df))
    
    del final_df['start_cord']
    
    final_df['latitude'] = pd.to_numeric(final_df['latitude'])
    final_df['longitude'] = pd.to_numeric(final_df['longitude'])
    final_df = final_df.reset_index(drop=True)
    
    coords = final_df.to_numpy()
    
    kms_per_radian = 6371.0088
    epsilon = 0.3 / kms_per_radian
    db = DBSCAN(eps=epsilon, min_samples=10,
                algorithm='ball_tree', metric='haversine').fit(np.radians(coords))
    cluster_labels = db.labels_
    num_clusters = len(set(cluster_labels))
    clusters = pd.Series([coords[cluster_labels == n] for n in range(num_clusters)])
    print('Number of clusters: {}'.format(num_clusters))

Imports

import pandas as pd, numpy as np, matplotlib.pyplot as plt
from sklearn.cluster import DBSCAN
from geopy.distance import great_circle
from shapely.geometry import MultiPoint
import haversine as hs