我有一个数据集，包括各个赛季的运动队表现，我从中创建了一个分离主队和客队的融合数据集。
原始数据集：
Div Date HomeTeam AwayTeam FTHG FTAG FTR HTHG HTAG HTR Referee
0 E0 11/08/17 Arsenal Leicester 4 3 H 2 2 D M Dean
1 E0 12/08/17 Brighton Man City 0 2 A 0 0 D M Oliver
2 E0 12/08/17 Chelsea Burnley 2 3 A 0 3 A C Pawson
融化数据集：
Div Date HomeTeam AwayTeam FTHG FTAG FTR HTHG HTAG \
0 E0 2017-08-11 Arsenal Leicester 4 3 H 2 2
1 E0 2017-08-11 Arsenal Leicester 4 3 H 2 2
2 E0 2017-08-12 Watford Liverpool 3 3 D 2 1
3 E0 2017-08-12 West Brom Bournemouth 1 0 H 1 0
4 E0 2017-08-12 Crystal Palace Huddersfield 0 3 A 0 2
HTR Referee Home/Away Team Opponent
0 D M Dean 1 Arsenal Leicester
1 D M Dean 0 Leicester Arsenal
2 H A Taylor 0 Liverpool Watford
3 H R Madley 0 Bournemouth West Brom
4 A J Moss 0 Huddersfield Crystal Palace
我还添加了额外的列来计算累积目标/诸如此类。实际上，它看起来像
Date Home/Away Team Opponent Cumg Cumc Result \
0 2017-08-11 1 Arsenal Leicester 0.0 0.0 1
1 2017-08-11 0 Leicester Arsenal 0.0 0.0 1
2 2017-08-12 0 Liverpool Watford 0.0 0.0 2
3 2017-08-12 0 Bournemouth West Brom 0.0 0.0 1
4 2017-08-12 0 Huddersfield Crystal Palace 0.0 0.0 0
Cumw Cuml Cumd Cumtr win_streak lose_streak
0 0.0 0.0 0.0 0.0 0.0 0.0
1 0.0 0.0 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0 0.0 0.0
3 0.0 0.0 0.0 0.0 0.0 0.0
4 0.0 0.0 0.0 0.0 0.0 0.0
我想＆＃34; unmelt＆＃34;返回原始格式的数据集，但保留我添加的新列。喜欢这个
Date Home/Away Team Opponent Cumg_team Cumc_team Result \
0 2017-08-11 1 Arsenal Leicester 0.0 0.0 1
1 2017-08-19 0 Arsenal Stoke 3.0 4.0 1
2 2017-08-27 0 Arsenal Liverpool 3.0 5.0 1
3 2017-09-09 1 Arsenal Bournemouth 3.0 9.0 1
4 2017-09-17 0 Arsenal Chelsea 3.0 12.0 2
Cumw_team Cuml_team Cumd Cumtr_team win_streak_team lose_streak_team \
0 0.0 0.0 0.0 0.0 0.0 0.0
1 0.0 1.0 0.0 0.0 0.0 0.0
2 0.0 2.0 0.0 0.0 1.0 0.0
3 0.0 3.0 0.0 0.0 0.0 0.0
4 0.0 4.0 0.0 0.0 0.0 0.0
Cumw_opponent Cuml_opponent Cumg_opponent Cumc_opponent Cumtr_opponent \
0 0.0 0.0 0.0 0.0 0.0
1 0.0 1.0 0.0 1.0 0.0
2 0.0 1.0 3.0 4.0 1.0
3 2.0 1.0 4.0 2.0 6.0
4 3.0 1.0 7.0 6.0 9.0
win_streak_opponent lose_streak_opponent
0 0.0 0.0
1 0.0 0.0
2 0.0 0.0
3 0.0 0.0
4 0.0 0.0
我可以通过
来做到这一点df1 = df[df['Team']=='Arsenal'].set_index('Date')
df2 = df[df['Opponent']=='Arsenal].set_index('Date')
df3 = df1.join(df2).reset_index()
但那不是很有效，我想知道无论如何我都可以用纯粹的类似SQL的操纵pandas df来做到这一点吗？
答案 0 :(得分：1)
似乎你需要：
@Multipart
@Headers({"Accept: application/json"})
@POST("api/save")
Call<SaveResponse> save(@Header("Authorization") String authorization,
@PartMap Map<String, RequestBody> map,
@Part List<MultipartBody.Part> files);