lat lng all_distances
0   39.984198   116.319322  0.000000
12  39.984611   116.319822  62.663690
24  39.984252   116.320826  128.601760
36  39.983916   116.320980  145.036185
48  39.982688   116.321225  233.518640
60  39.981441   116.321305  349.856365
72  39.980291   116.321430  469.693983

import pandas as pd
from geopy.distance import distance
print(pd.__version__)

data = [[ 39.984198, 116.319322],
       [ 39.984611, 116.319822],
       [ 39.984252, 116.320826],
       [ 39.983916, 116.32098 ],
       [ 39.982688, 116.321225],
       [ 39.981441, 116.321305],
       [ 39.980291, 116.32143 ],
       [ 39.979675, 116.321805],
       [ 39.979546, 116.322926],
       [ 39.979758, 116.324513]]

user_gps_log = pd.DataFrame(data, columns=['lat', 'lng'])

first_lat = user_gps_log.iloc[0].lat
first_lng = user_gps_log.iloc[0].lng
all_distances = user_gps_log.apply(lambda x: distance((x.lat, x.lng), (first_lat, first_lng)).m, axis=1)

user_gps_log['all_distances'] = all_distances

p = user_gps_log
i = 0
dist_thres = 2

while i < len(p):
    j = i+1
    while j < len(p):
        dist = distance((p.iloc[i].lat, p.iloc[i].lng), (p.iloc[j].lat, p.iloc[j].lng)).m
        if dist > dist_thres:
            # do stuff
            i = j
            token = 1
        break
    j = j+1

@njit
def cumsum_distance(lat, lng, limit=200):
    running_distance = 0
    first = (lat[0], lng[0])
    for i in range(lat.shape[0]):
        dist = distance(first, (lat[i], lng[i])).m
        running_distance += dist
        if running_distance > limit:
            yield i, running_distance
            running_distance = 0

runnig_distances = cumsum_distance(user_gps_log.lat.values, user_gps_log.lng.values, 200)

TypingError: Failed in nopython mode pipeline (step: nopython frontend)
Untyped global name 'distance': cannot determine Numba type of <class 'type'>

File "<ipython-input-194-7214618c7e64>", line 6:
def cumsum_distance(lat, lng, limit=200):
    <source elided>
    for i in range(lat.shape[0]):
        dist = distance(first, (lat[i], lng[i])).m
        ^

This is not usually a problem with Numba itself but instead often caused by
the use of unsupported features or an issue in resolving types.