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

Answer 2

Assuming that you want to compute haversine() with the first element in each user id group against all the other entries in a group, this approach will work:

# copying example data from OP
import pandas as pd
df = pd.read_clipboard() # alternately, df = pd.read_csv(filename)

def haversine_wrapper(row):
    # return None when both lon/lat pairs are the same
    if (row['first_lon'] == row['lon']) & (row['first_lat'] == row['lat']):
        return None
    return haversine(row['first_lon'], row['first_lat'], row['lon'], row['lat'])

df['result'] = (df.merge(df.groupby('user_id', as_index=False)
                           .agg({'lat':'first','lon':'first'})
                           .rename(columns={'lat':'first_lat','lon':'first_lon'}), 
                         on='user_id')
                  .apply(haversine_wrapper, axis='columns'))

print(df)

Output:

user_id        lat        lon     result
 0    1  19.111841  72.910729        NaN
 1    1  19.111342  72.908387   0.252243
 2    2  19.111542  72.907387        NaN
 3    2  19.137815  72.914085   3.004976
 4    2  19.119677  72.905081   0.936454
 5    2  19.129677  72.905081   2.031021
 6    3  19.319677  72.905081        NaN
 7    3  19.120217  72.907121  22.179974
 8    4  19.420217  72.807121        NaN
 9    4  19.520217  73.307121  53.584504
 10   5  19.319677  72.905081        NaN
 11   5  19.419677  72.805081  15.286775
 12   5  19.629677  72.705081  40.346128
 13   5  19.111860  72.911347  23.117560
 14   5  19.111860  72.931346  23.272178
 15   5  19.219677  72.605081  33.395165
 16   6  19.319677  72.805082        NaN
 17   6  19.419677  72.905086  15.287063

Answer 3

Try this approach:

import pandas as pd
import numpy as np

# parse CSV to DataFrame. You may want to specify the separator (`sep='...'`)
df = pd.read_csv('/path/to/file.csv')

# vectorized haversine function
def haversine(lat1, lon1, lat2, lon2, to_radians=True, earth_radius=6371):
    """
    slightly modified version: of http://stackoverflow.com/a/29546836/2901002

    Calculate the great circle distance between two points
    on the earth (specified in decimal degrees or in radians)

    All (lat, lon) coordinates must have numeric dtypes and be of equal length.

    """
    if to_radians:
        lat1, lon1, lat2, lon2 = np.radians([lat1, lon1, lat2, lon2])

    a = np.sin((lat2-lat1)/2.0)**2 + \
        np.cos(lat1) * np.cos(lat2) * np.sin((lon2-lon1)/2.0)**2

    return earth_radius * 2 * np.arcsin(np.sqrt(a))

Now we can calculate distances between coordinates belonging to the same id (group):

df['dist'] = \
    np.concatenate(df.groupby('id')
                     .apply(lambda x: haversine(x['lat'], x['lon'],
                                                x['lat'].shift(), x['lon'].shift())).values)

Result:

In [105]: df
Out[105]:
    id        lat        lon       dist
0    1  19.111841  72.910729        NaN
1    1  19.111342  72.908387   0.252243
2    2  19.111542  72.907387        NaN
3    2  19.137815  72.914085   3.004976
4    2  19.119677  72.905081   2.227658
5    2  19.129677  72.905081   1.111949
6    3  19.319677  72.905081        NaN
7    3  19.120217  72.907121  22.179974
8    4  19.420217  72.807121        NaN
9    4  19.520217  73.307121  53.584504
10   5  19.319677  72.905081        NaN
11   5  19.419677  72.805081  15.286775
12   5  19.629677  72.705081  25.594890
13   5  19.111860  72.911347  61.509917
14   5  19.111860  72.931346   2.101215
15   5  19.219677  72.605081  36.304756
16   6  19.319677  72.805082        NaN
17   6  19.419677  72.905086  15.287063

Answer 4

This should work exactly like your sample input and output.

SCRIPT

import csv
from haversine import haversine

with open('file.csv') as file:

    reader = csv.reader(file)
    next(reader) # skip header
    previous_row = (None, None, None)
    for id, lon, lat in reader:

        id, lon, lat = int(id), float(lon), float(lat)
        current_row = id, lon, lat
        distance = float('nan')

        if current_row[0] == previous_row[0]:
            distance = haversine(previous_row[1:], current_row[1:])

        print('{} {:02.7f} {:02.7f} {:02.7f}'.format(*current_row, distance))
        previous_row = current_row

OUTPUT

1 19.1118410 72.9107290 nan
1 19.1113420 72.9083870 0.2522433
2 19.1115420 72.9073870 nan
2 19.1378150 72.9140850 3.0049762
2 19.1196770 72.9050810 2.2276576
2 19.1296770 72.9050810 1.1119493
3 19.3196770 72.9050810 nan
3 19.1202170 72.9071210 22.1799743
4 19.4202170 72.8071210 nan
4 19.5202170 73.3071210 53.5845041
5 19.3196770 72.9050810 nan
5 19.4196770 72.8050810 15.2867753
5 19.6296770 72.7050810 25.5948897
5 19.1118600 72.9113470 61.5099175
5 19.1118600 72.9313460 2.1012148
5 19.2196770 72.6050810 36.3047557
6 19.3196770 72.8050820 nan
6 19.4196770 72.9050860 15.2870632