示例CSV如下所示：
user_id lat lon
1 19.111841 72.910729
1 19.111342 72.908387
2 19.111542 72.907387
2 19.137815 72.914085
2 19.119677 72.905081
2 19.129677 72.905081
3 19.319677 72.905081
3 19.120217 72.907121
4 19.420217 72.807121
4 19.520217 73.307121
5 19.319677 72.905081
5 19.419677 72.805081
5 19.629677 72.705081
5 19.111860 72.911347
5 19.111860 72.931346
5 19.219677 72.605081
6 19.319677 72.805082
6 19.419677 72.905086
我知道我可以使用 hasrsine 进行距离计算（而且python也有hasrsine包）：
def haversine(lon1, lat1, lon2, lat2):
"""
Calculate the great circle distance between two points
on the earth (specified in decimal degrees).
Source: http://gis.stackexchange.com/a/56589/15183
"""
# convert decimal degrees to radians
lon1, lat1, lon2, lat2 = map(math.radians, [lon1, lat1, lon2, lat2])
# haversine formula
dlon = lon2 - lon1
dlat = lat2 - lat1
a = math.sin(dlat/2)**2 + math.cos(lat1) * math.cos(lat2) * math.sin(dlon/2)**2
c = 2 * math.asin(math.sqrt(a))
km = 6371 * c
return km
但是，我只想计算相同ID 内的距离。 所以预期的答案是这样的：
user_id lat lon result
1 19.111841 72.910729 NaN
1 19.111342 72.908387 xx*
2 19.111542 72.907387 NaN
2 19.137815 72.914085 xx
2 19.119677 72.905081 xx
2 19.129677 72.905081 xx
3 19.319677 72.905081 NaN
3 19.120217 72.907121 xx
4 19.420217 72.807121 NaN
4 19.520217 73.307121 xx
5 19.319677 72.905081 NaN
5 19.419677 72.805081 xx
5 19.629677 72.705081 xx
5 19.111860 72.911347 xx
5 19.111860 72.931346 xx
5 19.219677 72.605081 xx
6 19.319677 72.805082 NaN
6 19.419677 72.905086 xx
*：xx是以km为单位的距离数。
我该怎么做？
答案 0 :(得分：6)
尝试这种方法：
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))
现在我们可以计算属于同一id（组）的坐标之间的距离：
df['dist'] = \
np.concatenate(df.groupby('id')
.apply(lambda x: haversine(x['lat'], x['lon'],
x['lat'].shift(), x['lon'].shift())).values)
结果：
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
答案 1 :(得分：0)
你只需要一个工作数据结构，列表和lat / lon作为元组。快速原型化它可能看起来像这样：
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]))
返回：
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
答案 2 :(得分：0)
这应该与您的示例输入和输出完全一样。
<强> 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
<强> 输出
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
答案 3 :(得分：0)
假设您要使用每个用户ID组中的第一个元素与组中的所有其他条目计算haversine()，这种方法将起作用：
# 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)
输出：
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