Python中文网 - 问答频道, 解决您学习工作中的Python难题和Bug
Python常见问题
我写这个问题是因为绝望
我有以下资料：
FORECAST_DATE BOOK_SEGMENT_ID PERIOD_END_P_n_L PERIOD_END_AQR_AVERAGE_BALANCE PERIOD_END_AQR_SPOT_BALANCE
0 Y-1 1 4563737.210000000000000000 33.567583016668937929 41.353567454639780837
1 Y-1 6 315406.100000000000000000 3.061750829952636692 2.522829568693654370
2 Y-1 4 15082237.880000000000000000 138.612207176403403592 181.732357082243759483
3 Y-1 3 43569174.010000000000000000 359.781133882318363070 566.852017680466803209
4 Y-1 2 494389977.810000000000000000 3706.315488833574075112 5882.954828234764105261
5 Y0 1 4626548.470000000000000000 33.463511078561497885 42.108630195163517588
6 Y0 4 57181865.440000000000000000 527.018846167262073635 720.788515365844542397
7 Y0 6 1580093.370000000000000000 14.088379421245762666 16.099449242348320563
8 Y0 3 179134588.050000000000000000 1456.303254133717454069 2216.535521013819977797
9 Y0 2 1845235987.940000000000000000 13967.821066500684233312 22474.822328214837177745
10 Y1 2 1845235987.940000000000000000 13967.821066500684233312 22474.822328214837177745
11 Y1 4 57181865.440000000000000000 527.018846167262073635 720.788515365844542397
12 Y1 3 179134588.050000000000000000 1456.303254133717454069 2216.535521013819977797
13 Y1 6 1580093.370000000000000000 14.088379421245762666 16.099449242348320563
14 Y1 1 4626548.470000000000000000 33.463511078561497885 42.108630195163517588
15 Y2 1 4626548.470000000000000000 33.463511078561497885 42.108630195163517588
16 Y2 2 1845235987.940000000000000000 13967.821066500684233312 22474.822328214837177745
17 Y2 4 57181865.440000000000000000 527.018846167262073635 720.788515365844542397
18 Y2 3 179134588.050000000000000000 1456.303254133717454069 2216.535521013819977797
19 Y2 6 1580093.370000000000000000 14.088379421245762666 16.099449242348320563
20 Y3 4 57181865.440000000000000000 527.018846167262073635 720.788515365844542397
21 Y3 1 4626548.470000000000000000 33.463511078561497885 42.108630195163517588
22 Y3 2 1845235987.940000000000000000 13967.821066500684233312 22474.822328214837177745
23 Y3 6 1580093.370000000000000000 14.088379421245762666 16.099449242348320563
24 Y3 3 179134588.050000000000000000 1456.303254133717454069 2216.535521013819977797
期望的结果是：
FORECAST_DATE
PERIOD_END_P_n_L Y-1 Y0 Y1 Y2 Y3
BOOK_SEGMENT_ID
1 4563737.210000000000000000 4626548.470000000000000000 4626548.470000000000000000 4626548.470000000000000000 4626548.470000000000000000
2 494389977.810000000000000000 1845235987.940000000000000000 1845235987.940000000000000000 1845235987.940000000000000000 1845235987.940000000000000000
3 43569174.010000000000000000 179134588.050000000000000000 179134588.050000000000000000 179134588.050000000000000000 179134588.050000000000000000
4 15082237.880000000000000000 57181865.440000000000000000 57181865.440000000000000000 57181865.440000000000000000 57181865.440000000000000000
6 315406.100000000000000000 1580093.370000000000000000 1580093.370000000000000000 1580093.370000000000000000 1580093.370000000000000000
PERIOD_END_AQR_AVERAGE_BALANCE
BOOK_SEGMENT_ID
1 4563737.210000000000000000 4626548.470000000000000000 4626548.470000000000000000 4626548.470000000000000000 4626548.470000000000000000
2 494389977.810000000000000000 1845235987.940000000000000000 1845235987.940000000000000000 1845235987.940000000000000000 1845235987.940000000000000000
3 43569174.010000000000000000 179134588.050000000000000000 179134588.050000000000000000 179134588.050000000000000000 179134588.050000000000000000
4 15082237.880000000000000000 57181865.440000000000000000 57181865.440000000000000000 57181865.440000000000000000 57181865.440000000000000000
6 315406.100000000000000000 1580093.370000000000000000 1580093.370000000000000000 1580093.370000000000000000 1580093.370000000000000000
PERIOD_END_AQR_SPOT_BALANCE
BOOK_SEGMENT_ID
1 4563737.210000000000000000 4626548.470000000000000000 4626548.470000000000000000 4626548.470000000000000000 4626548.470000000000000000
2 494389977.810000000000000000 1845235987.940000000000000000 1845235987.940000000000000000 1845235987.940000000000000000 1845235987.940000000000000000
3 43569174.010000000000000000 179134588.050000000000000000 179134588.050000000000000000 179134588.050000000000000000 179134588.050000000000000000
4 15082237.880000000000000000 57181865.440000000000000000 57181865.440000000000000000 57181865.440000000000000000 57181865.440000000000000000
6 315406.100000000000000000 1580093.370000000000000000 1580093.370000000000000000 1580093.370000000000000000 1580093.370000000000000000
请忽略这些值，它们应该是从其各自的时段{stat}开始的原始数据帧。重要的是获取格式。
到目前为止，我尝试了：
forecast_summary.pivot(columns="FORECAST_DATE", index="BOOK_SEGMENT_ID")
RESULT:
PERIOD_END_P_n_L PERIOD_END_AQR_AVERAGE_BALANCE PERIOD_END_AQR_SPOT_BALANCE
FORECAST_DATE Y-1 Y0 Y1 Y2 Y3 Y-1 Y0 Y1 Y2 Y3 Y-1 Y0 Y1 Y2 Y3
BOOK_SEGMENT_ID
1 4563737.210000000000000000 4626548.470000000000000000 4626548.470000000000000000 4626548.470000000000000000 4626548.470000000000000000 33.567583016668937929 33.463511078561497885 33.463511078561497885 33.463511078561497885 33.463511078561497885 41.353567454639780837 42.108630195163517588 42.108630195163517588 42.108630195163517588 42.108630195163517588
2 494389977.810000000000000000 1845235987.940000000000000000 1845235987.940000000000000000 1845235987.940000000000000000 1845235987.940000000000000000 3706.315488833574075112 13967.821066500684233312 13967.821066500684233312 13967.821066500684233312 13967.821066500684233312 5882.954828234764105261 22474.822328214837177745 22474.822328214837177745 22474.822328214837177745 22474.822328214837177745
3 43569174.010000000000000000 179134588.050000000000000000 179134588.050000000000000000 179134588.050000000000000000 179134588.050000000000000000 359.781133882318363070 1456.303254133717454069 1456.303254133717454069 1456.303254133717454069 1456.303254133717454069 566.852017680466803209 2216.535521013819977797 2216.535521013819977797 2216.535521013819977797 2216.535521013819977797
4 15082237.880000000000000000 57181865.440000000000000000 57181865.440000000000000000 57181865.440000000000000000 57181865.440000000000000000 138.612207176403403592 527.018846167262073635 527.018846167262073635 527.018846167262073635 527.018846167262073635 181.732357082243759483 720.788515365844542397 720.788515365844542397 720.788515365844542397 720.788515365844542397
6 315406.100000000000000000 1580093.370000000000000000 1580093.370000000000000000 1580093.370000000000000000 1580093.370000000000000000 3.061750829952636692 14.088379421245762666 14.088379421245762666 14.088379421245762666 14.088379421245762666 2.522829568693654370 16.099449242348320563 16.099449242348320563 16.099449242348320563 16.099449242348320563
forecast_summary.set_index(["BOOK_SEGMENT_ID"], inplace=True).T
RESULT:
BOOK_SEGMENT_ID 1 6 4 3 2 1 4 6 3 2 2 4 3 6 1 1 2 4 3 6 4 1 2 6 3
FORECAST_DATE Y-1 Y-1 Y-1 Y-1 Y-1 Y0 Y0 Y0 Y0 Y0 Y1 Y1 Y1 Y1 Y1 Y2 Y2 Y2 Y2 Y2 Y3 Y3 Y3 Y3 Y3
PERIOD_END_P_n_L 4563737.210000000000000000 315406.100000000000000000 15082237.880000000000000000 43569174.010000000000000000 494389977.810000000000000000 4626548.470000000000000000 57181865.440000000000000000 1580093.370000000000000000 179134588.050000000000000000 1845235987.940000000000000000 1845235987.940000000000000000 57181865.440000000000000000 179134588.050000000000000000 1580093.370000000000000000 4626548.470000000000000000 4626548.470000000000000000 1845235987.940000000000000000 57181865.440000000000000000 179134588.050000000000000000 1580093.370000000000000000 57181865.440000000000000000 4626548.470000000000000000 1845235987.940000000000000000 1580093.370000000000000000 179134588.050000000000000000
PERIOD_END_AQR_AVERAGE_BALANCE 33.567583016668937929 3.061750829952636692 138.612207176403403592 359.781133882318363070 3706.315488833574075112 33.463511078561497885 527.018846167262073635 14.088379421245762666 1456.303254133717454069 13967.821066500684233312 13967.821066500684233312 527.018846167262073635 1456.303254133717454069 14.088379421245762666 33.463511078561497885 33.463511078561497885 13967.821066500684233312 527.018846167262073635 1456.303254133717454069 14.088379421245762666 527.018846167262073635 33.463511078561497885 13967.821066500684233312 14.088379421245762666 1456.303254133717454069
PERIOD_END_AQR_SPOT_BALANCE 41.353567454639780837 2.522829568693654370 181.732357082243759483 566.852017680466803209 5882.954828234764105261 42.108630195163517588 720.788515365844542397 16.099449242348320563 2216.535521013819977797 22474.822328214837177745 22474.822328214837177745 720.788515365844542397 2216.535521013819977797 16.099449242348320563 42.108630195163517588 42.108630195163517588 22474.822328214837177745 720.788515365844542397 2216.535521013819977797 16.099449242348320563 720.788515365844542397 42.108630195163517588 22474.822328214837177745 16.099449242348320563 2216.535521013819977797
我尝试了许多与.T.reset\u index.set\u index.pivot的组合和变体，但都未能获得正确的输出表
如果你张贴不同的组合，我可以尝试他们，说它是否工作，并张贴结果，你不需要测试你的变化，如果你不想
谢谢
更新：
我得到了一个很接近的结果：
BOOK_SEGMENT_ID 1 2 3 4 6
FORECAST_DATE
PERIOD_END_P_n_L Y-1 4563737.210000000000000000 494389977.810000000000000000 43569174.010000000000000000 15082237.880000000000000000 315406.100000000000000000
Y0 4626548.470000000000000000 1845235987.940000000000000000 179134588.050000000000000000 57181865.440000000000000000 1580093.370000000000000000
Y1 4626548.470000000000000000 1845235987.940000000000000000 179134588.050000000000000000 57181865.440000000000000000 1580093.370000000000000000
Y2 4626548.470000000000000000 1845235987.940000000000000000 179134588.050000000000000000 57181865.440000000000000000 1580093.370000000000000000
Y3 4626548.470000000000000000 1845235987.940000000000000000 179134588.050000000000000000 57181865.440000000000000000 1580093.370000000000000000
PERIOD_END_AQR_AVERAGE_BALANCE Y-1 33.567583016668937929 3706.315488833574075112 359.781133882318363070 138.612207176403403592 3.061750829952636692
Y0 33.463511078561497885 13967.821066500684233312 1456.303254133717454069 527.018846167262073635 14.088379421245762666
Y1 33.463511078561497885 13967.821066500684233312 1456.303254133717454069 527.018846167262073635 14.088379421245762666
Y2 33.463511078561497885 13967.821066500684233312 1456.303254133717454069 527.018846167262073635 14.088379421245762666
Y3 33.463511078561497885 13967.821066500684233312 1456.303254133717454069 527.018846167262073635 14.088379421245762666
PERIOD_END_AQR_SPOT_BALANCE Y-1 41.353567454639780837 5882.954828234764105261 566.852017680466803209 181.732357082243759483 2.522829568693654370
Y0 42.108630195163517588 22474.822328214837177745 2216.535521013819977797 720.788515365844542397 16.099449242348320563
Y1 42.108630195163517588 22474.822328214837177745 2216.535521013819977797 720.788515365844542397 16.099449242348320563
Y2 42.108630195163517588 22474.822328214837177745 2216.535521013819977797 720.788515365844542397 16.099449242348320563
Y3 42.108630195163517588 22474.822328214837177745 2216.535521013819977797 720.788515365844542397 16.099449242348320563
我知道了：
forecast_summary.pivot(columns="FORECAST_DATE", index="BOOK_SEGMENT_ID").T
现在将尝试交换预测日期和预订段ID
更新2:
差不多了
forecast_summary.pivot(columns="FORECAST_DATE", index="BOOK_SEGMENT_ID").T.stack().reset_index(level=1).pivot(columns="FORECAST_DATE")
RESULT:
0
FORECAST_DATE Y-1 Y0 Y1 Y2 Y3
BOOK_SEGMENT_ID
PERIOD_END_AQR_AVERAGE_BALANCE 1 33.567583016668937929 33.463511078561497885 33.463511078561497885 33.463511078561497885 33.463511078561497885
2 3706.315488833574075112 13967.821066500684233312 13967.821066500684233312 13967.821066500684233312 13967.821066500684233312
3 359.781133882318363070 1456.303254133717454069 1456.303254133717454069 1456.303254133717454069 1456.303254133717454069
4 138.612207176403403592 527.018846167262073635 527.018846167262073635 527.018846167262073635 527.018846167262073635
6 3.061750829952636692 14.088379421245762666 14.088379421245762666 14.088379421245762666 14.088379421245762666
PERIOD_END_AQR_SPOT_BALANCE 1 41.353567454639780837 42.108630195163517588 42.108630195163517588 42.108630195163517588 42.108630195163517588
2 5882.954828234764105261 22474.822328214837177745 22474.822328214837177745 22474.822328214837177745 22474.822328214837177745
3 566.852017680466803209 2216.535521013819977797 2216.535521013819977797 2216.535521013819977797 2216.535521013819977797
4 181.732357082243759483 720.788515365844542397 720.788515365844542397 720.788515365844542397 720.788515365844542397
6 2.522829568693654370 16.099449242348320563 16.099449242348320563 16.099449242348320563 16.099449242348320563
PERIOD_END_P_n_L 1 4563737.210000000000000000 4626548.470000000000000000 4626548.470000000000000000 4626548.470000000000000000 4626548.470000000000000000
2 494389977.810000000000000000 1845235987.940000000000000000 1845235987.940000000000000000 1845235987.940000000000000000 1845235987.940000000000000000
3 43569174.010000000000000000 179134588.050000000000000000 179134588.050000000000000000 179134588.050000000000000000 179134588.050000000000000000
4 15082237.880000000000000000 57181865.440000000000000000 57181865.440000000000000000 57181865.440000000000000000 57181865.440000000000000000
6 315406.100000000000000000 1580093.370000000000000000 1580093.370000000000000000 1580093.370000000000000000 1580093.370000000000000000
可能是非常难看的代码行，但现在需要弄清楚如何从列标题中删除0
Tags： iddateindexsegmentperiodendaveragebalance
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过了一会儿
产生：
首先我设置两列的索引，然后转置将这两列作为列标题。然后堆栈，获取第二个级别并将其作为列放置
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