我有以下数据框.

df.head(30)

     struct_id  resNum score_type_name  score_value
0   4294967297       1           omega     0.064840
1   4294967297       1          fa_dun     2.185618
2   4294967297       1      fa_dun_dev     0.000027
3   4294967297       1     fa_dun_semi     2.185591
4   4294967297       1             ref    -1.191180
5   4294967297       2            rama    -0.795161
6   4294967297       2           omega     0.222345
7   4294967297       2          fa_dun     1.378923
8   4294967297       2      fa_dun_dev     0.028560
9   4294967297       2      fa_dun_rot     1.350362
10  4294967297       2         p_aa_pp    -0.442467
11  4294967297       2             ref     0.249477
12  4294967297       3            rama     0.267443
13  4294967297       3           omega     0.005106
14  4294967297       3          fa_dun     0.020352
15  4294967297       3      fa_dun_dev     0.025507
16  4294967297       3      fa_dun_rot    -0.005156
17  4294967297       3         p_aa_pp    -0.096847
18  4294967297       3             ref     0.979644
19  4294967297       4            rama    -1.403292
20  4294967297       4           omega     0.212160
21  4294967297       4          fa_dun     4.218029
22  4294967297       4      fa_dun_dev     0.003712
23  4294967297       4     fa_dun_semi     4.214317
24  4294967297       4         p_aa_pp    -0.462765
25  4294967297       4             ref    -1.960940
26  4294967297       5            rama    -0.600053
27  4294967297       5           omega     0.061867
28  4294967297       5          fa_dun     3.663050
29  4294967297       5      fa_dun_dev     0.004953

根据 pivot 文档，我应该能够使用 pivot 函数在 score_type_name 上重塑它.

According to the pivot documentation, I should be able to reshape this on the score_type_name using the pivot function.

df.pivot(columns='score_type_name',values='score_value',index=['struct_id','resNum'])

但是，我得到以下信息.

But, I get the following.

但是，pivot_table 函数似乎可以工作:

However, pivot_table function seems to work:

pivoted = df.pivot_table(columns='score_type_name',
                         values='score_value',
                         index=['struct_id','resNum'])

但至少对我来说，它不适合做进一步的分析.我希望它只将 struct_id、resNum 和 score_type_name 作为列，而不是将 score_type_name 堆叠在其他列的顶部.此外，我希望 struct_id 用于每一行，而不是像在表中那样聚合在连接行中.

But it does not lend itself, for me atleast, to further analysis. I want it to just have the struct_id, resNum, and score_type_name as columns instead of stacking the score_type_name on top of the other columns. Additionally, I want the struct_id to be for every row, and not aggregate in a joined row like it does for the table.

那么谁能告诉我如何使用 pivot 获得一个不错的 Dataframe?此外，从文档中，我无法说出为什么 pivot_table 有效而 pivot 无效.如果我查看枢轴的第一个示例，它看起来正是我所需要的.

So can anyone tell me how I can get a nice Dataframe like I want using pivot? Additionally, from the documentation, I can't tell why pivot_table works and pivot doesn't. If I look at the first example of pivot, it looks like exactly what I need.

附:我确实发布了一个关于这个问题的问题，但是我在演示输出方面做得很差，我删除了它并再次使用 ipython notebook 尝试.如果您看到两次，我提前道歉.

P.S. I did post a question in reference to this problem, but I did such a poor job of demonstrating the output, I deleted it and tried again using ipython notebook. I apologize in advance if you are seeing this twice.

这是供您参考的笔记本

编辑 - 我想要的结果看起来像这样(用 excel 制作):

EDIT - My desired results would look like this (made in excel):

StructId    resNum  pdb_residue_number  chain_id    name3   fa_dun  fa_dun_dev  fa_dun_rot  fa_dun_semi omega   p_aa_pp rama    ref
4294967297  1   99  A   ASN 2.1856  0.0000      2.1856  0.0648          -1.1912
4294967297  2   100 A   MET 1.3789  0.0286  1.3504      0.2223  -0.4425 -0.7952 0.2495
4294967297  3   101 A   VAL 0.0204  0.0255  -0.0052     0.0051  -0.0968 0.2674  0.9796
4294967297  4   102 A   GLU 4.2180  0.0037      4.2143  0.2122  -0.4628 -1.4033 -1.9609
4294967297  5   103 A   GLN 3.6630  0.0050      3.6581  0.0619  -0.2759 -0.6001 -1.5172
4294967297  6   104 A   MET 1.5175  0.2206  1.2968      0.0504  -0.3758 -0.7419 0.2495
4294967297  7   105 A   HIS 3.6987  0.0184      3.6804  0.0547  0.4019  -0.1489 0.3883
4294967297  8   106 A   THR 0.1048  0.0134  0.0914      0.0003  -0.7963 -0.4033 0.2013
4294967297  9   107 A   ASP 2.3626  0.0005      2.3620  0.0521  0.1955  -0.3499 -1.6300
4294967297  10  108 A   ILE 1.8447  0.0270  1.8176      0.0971  0.1676  -0.4071 1.0806
4294967297  11  109 A   ILE 0.1276  0.0092  0.1183      0.0208  -0.4026 -0.0075 1.0806
4294967297  12  110 A   SER 0.2921  0.0342  0.2578      0.0342  -0.2426 -1.3930 0.1654
4294967297  13  111 A   LEU 0.6483  0.0019  0.6464      0.0845  -0.3565 -0.2356 0.7611
4294967297  14  112 A   TRP 2.5965  0.1507      2.4457  0.5143  -0.1370 -0.5373 1.2341
4294967297  15  113 A   ASP 2.6448  0.1593          0.0510      -0.5011 

推荐答案

对于pivot和pivot_table的区别还有兴趣的朋友，主要有两个区别:

For anyone who is still interested in the difference between pivot and pivot_table, there are mainly two differences:

pivot_table 是 pivot 的概括，它可以处理一个 pivot 索引/列对的重复值.具体来说，您可以使用关键字参数 aggfunc 为 pivot_table 提供一个聚合函数列表.pivot_table 的默认 aggfunc 是 numpy.mean.pivot_table 还支持对pivot 表的索引和列使用多列.系统会自动为您生成分层索引. pivot_table is a generalization of pivot that can handle duplicate values for one pivoted index/column pair. Specifically, you can give pivot_table a list of aggregation functions using keyword argument aggfunc. The default aggfunc of pivot_table is numpy.mean. pivot_table also supports using multiple columns for the index and column of the pivoted table. A hierarchical index will be automatically generated for you.

参考:pivot 和 pivot_table