如何将函数应用于每组数据框

Question

如何在groupby数据框上应用函数

给定数据框 df。

userid   trip_id        lat         long
141.0      1.0      39.979547   116.306813
141.0      1.0      39.979558   116.306823
141.0      1.0      39.979575   116.306835
141.0      1.0      39.979587   116.306847
141.0      2.0      39.979603   116.306852
141.0      2.0      39.979612   116.306867
141.0      2.0      39.979627   116.306877
141.0      2.0      39.979635   116.306888
141.0      3.0      39.979645   116.306903
141.0      3.0      39.979657   116.306913
141.0      3.0      39.979670   116.306920
141.0      3.0      39.979682   116.306920

我想计算每组数据帧的 Vincenty 距离。数据框分为 2 列，即 (userid,trip_id)

我可以通过给定的语句计算完整数据帧的 vincenty 距离

from geopy.distance import vincenty
df['lat_next'] = df['lat'].shift(-1)
df['long_next'] = df['long'].shift(-1)
df['Vincenty_distance'] = df.dropna().apply(lambda x: vincenty((x['lat'], x['long']), (x['lat_next'], x['long_next'])).meters, axis = 1)
df = df.drop(['lat_next','long_next'], axis=1) 

我想将此功能应用于每个组，我尝试使用此语句但出现错误。

df['Vincenty_distance'] = df.dropna().groupby(['userid','trip_id']).apply(lambda x: vincenty((x['lat'], x['long']), (x['lat_next'], x['long_next'])).meters,axis=1)

我期待以下结果。

userid  trip_id        lat        long        Vincenty_distance
141.0      1.0      39.979547   116.306813         2.563812
141.0      1.0      39.979558   116.306823         2.956183
141.0      1.0      39.979575   116.306835         2.332577
141.0      1.0      39.979587   116.306847           Nan
141.0      2.0      39.979603   116.306852         2.334821
141.0      2.0      39.979612   116.306867         2.332577
141.0      2.0      39.979627   116.306877         1.695449
141.0      2.0      39.979635   116.306888           Nan
141.0      3.0      39.979645   116.306903          1.871784
141.0      3.0      39.979657   116.306913         1.982752
141.0      3.0      39.979670   116.306920         2.220685
141.0      3.0      39.979682   116.306920           Nan

Answer 1

我相信您需要首先DataFrameGroupBy.shift对列进行每组的轮班next，因此groupby不需要vincenty：

df = df.join(df.groupby(['userid','trip_id'])[['lat','long']].shift(-1).add_suffix('_next'))
print (df)
    userid  trip_id        lat        long   lat_next   long_next
0    141.0      1.0  39.979547  116.306813  39.979558  116.306823
1    141.0      1.0  39.979558  116.306823  39.979575  116.306835
2    141.0      1.0  39.979575  116.306835  39.979587  116.306847
3    141.0      1.0  39.979587  116.306847        NaN         NaN
4    141.0      2.0  39.979603  116.306852  39.979612  116.306867
5    141.0      2.0  39.979612  116.306867  39.979627  116.306877
6    141.0      2.0  39.979627  116.306877  39.979635  116.306888
7    141.0      2.0  39.979635  116.306888        NaN         NaN
8    141.0      3.0  39.979645  116.306903  39.979657  116.306913
9    141.0      3.0  39.979657  116.306913  39.979670  116.306920
10   141.0      3.0  39.979670  116.306920  39.979682  116.306920
11   141.0      3.0  39.979682  116.306920        NaN         NaN

f = lambda x: vincenty((x['lat'], x['long']), (x['lat_next'], x['long_next'])).meters
df['Vincenty_distance'] = df.dropna().apply(f, axis = 1)
df = df.drop(['lat_next','long_next'], axis=1) 
print (df)
    userid  trip_id        lat        long  Vincenty_distance
0    141.0      1.0  39.979547  116.306813           1.490437
1    141.0      1.0  39.979558  116.306823           2.147940
2    141.0      1.0  39.979575  116.306835           1.681071
3    141.0      1.0  39.979587  116.306847                NaN
4    141.0      2.0  39.979603  116.306852           1.624902
5    141.0      2.0  39.979612  116.306867           1.871784
6    141.0      2.0  39.979627  116.306877           1.293017
7    141.0      2.0  39.979635  116.306888                NaN
8    141.0      3.0  39.979645  116.306903           1.582706
9    141.0      3.0  39.979657  116.306913           1.562388
10   141.0      3.0  39.979670  116.306920           1.332411
11   141.0      3.0  39.979682  116.306920                NaN