我的车辆跟踪系统有一组数据，需要我根据纬度和经度计算距离。理解使用Haversine公式可以帮助获取行之间的距离，但是由于需要基于2个字段（模型类型和模式）的距离，因此我有些困惑。
如下所示是我的代码：
def haversine(lat1,lon1,lat2,lon2, to_radians = True, earth_radius =6371):
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))
mydataset = pd.read_csv(x + '.txt')
print (mydataset.shape)
mydataset = mydataset.sort_values(by=['Model','timestamp']) #sort
mydataset['dist'] =
np.concatenate(mydataset.groupby(["Model"]).apply(lambda
x: haversine(x['Latitude'],x['Longitude'],
x['Latitude'].shift(),x['Longitude'].shift())).values)
有了这个，我能够基于模型（通过使用排序）计算行之间的距离。
但我想更进一步，根据“模式”和“模型”进行计算。我的字段是“索引，模型，模式，纬度，经度，时间戳”
请咨询！
Index, Model, Timestamp, Long, Lat, Mode(denote as 0 or 2), Distance Calculated
1, X, 2018-01-18 09:16:37.070, 103.87772815, 1.35653496, 0, 0.0
2, X, 2018-01-18 09:16:39.071, 103.87772815, 1.35653496, 0, 0.0
3, X, 2018-01-18 09:16:41.071, 103.87772815, 1.35653496, 0, 0.0
4, X, 2018-01-18-09:16:43.071, 103.87772052, 1.35653496, 0, 0.0008481795
5, X, 2018-01-18 09:16:45.071, 103.87770526, 1.35653329, 0, 0.0017064925312804799
6, X, 2018-01-18 09:16:51.070, 103.87770526, 1.35653329, 2, 0.0
7, X, 2018-01-18 09:16:53.071, 103.87770526, 1.35653329, 2, 0.0
8, X, 2018-01-18 09:59:55.072, 103.87770526, 1.35652828, 0, 0.0005570865824842293
无论使用哪种模式，我都需要它来计算模型的总行程距离以及模型的总行程距离
答案 0 :(得分：0)
我认为需要添加DataFrame构造函数才能起作用，然后向groupby添加另一个列名，例如["Model", "Mode(denote as 0 or 2)"]或["Model", "Mode"]，并按列名添加
def haversine(lat1,lon1,lat2,lon2, to_radians = True, earth_radius =6371):
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 pd.DataFrame(earth_radius *2 * np.arcsin(np.sqrt(a)))
mydataset['dist'] = (mydataset.groupby(["Model", "Mode(denote as 0 or 2)"])
.apply(lambda x: haversine(x['Lat'],
x['Long'],
x['Lat'].shift(),
x['Long'].shift())).values)
#if need replace NaNs to 0
mydataset['dist'] = mydataset['dist'].fillna(0)
print (mydataset)
Index Model Timestamp Long Lat \
0 1 X 2018-01-18 09:16:37.070 103.877728 1.356535
1 2 X 2018-01-18 09:16:39.071 103.877728 1.356535
2 3 X 2018-01-18 09:16:41.071 103.877728 1.356535
3 4 X 2018-01-18 09:16:43.071 103.877721 1.356535
4 5 X 2018-01-18 09:16:45.071 103.877705 1.356533
5 6 X 2018-01-18 09:16:51.070 103.877705 1.356533
6 7 X 2018-01-18 09:16:53.071 103.877705 1.356533
7 8 X 2018-01-18 09:59:55.072 103.877705 1.356528
Mode(denote as 0 or 2) Distance Calculated dist
0 0 0.000000 0.000000
1 0 0.000000 0.000000
2 0 0.000000 0.000000
3 0 0.000848 0.000848
4 0 0.001706 0.001706
5 2 0.000000 0.000557
6 2 0.000000 0.000000
7 0 0.000557 0.000000