Pandas: calculate haversine distance within each group of rows

Question

The sample CSV is like this:

 user_id  lat         lon
    1   19.111841   72.910729
    1   19.111342   72.908387
    2   19.111542   72.907387
    2   19.1         


        

Answer 1

You just need a working data structure, dict of lists and lat/lon as tuples. Quickly prototyped it could look like this:

from haversine import haversine  # pip3 install haversine
from collections import defaultdict

csv = """
1   19.111841   72.910729
1   19.111342   72.908387
2   19.111342   72.908387
2   19.137815   72.914085
2   19.119677   72.905081
2   19.119677   72.905081
3   19.119677   72.905081
3   19.120217   72.907121
5   19.119677   72.905081
5   19.119677   72.905081
5   19.119677   72.905081
5   19.111860   72.911346
5   19.111860   72.911346
5   19.119677   72.905081
6   19.119677   72.905081
6   19.119677   72.905081
"""

d = defaultdict(list)  # data structure !

for line in csv.splitlines():
    line = line.strip()  # remove whitespaces

    if not line:
        continue  # skip empty lines

    cId, lat, lon = line.split('   ')
    d[cId].append((float(lat), float(lon)))

for k, v in d.items():
    print ('Distance for id: ', k, haversine(v[0], v[1]))

returns:

Distance for id:  1 0.2522433072207346
Distance for id:  2 3.0039140173887557
Distance for id:  3 0.22257643412844885
Distance for id:  5 0.0
Distance for id:  6 0.0