pandas :数据透视表和数据透视表之间的区别.为什么只有pivot_table有效? [英] Pandas: Difference between pivot and pivot_table. Why is only pivot_table working?
问题描述
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
根据数据透视文档，我应该能够使用数据透视功能在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函数似乎可以正常工作:
However, pivot_table function seems to work:
pivoted = df.pivot_table(columns='score_type_name',
values='score_value',
index=['struct_id','resNum'])
但对于我来说，它至少不适合进行进一步的分析.我希望它仅将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.另外，我希望struct_id适用于每一行，而不是像表一样将其聚集在连接的行中.
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像使用透视图那样获得一个不错的数据框?另外，从文档中，我无法确定为什么pivot_table有效，而pivot无法起作用.如果我看一下第一个数据透视图示例，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.
P.S. 我确实发布了一个有关此问题的问题，但是我在演示输出方面做得很差，我删除了它，然后使用ipython notebook重新尝试.如果您两次看到此邮件，我先表示歉意.
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.
编辑-我想要的结果将如下所示(在excel中制作):
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
我不确定我是否理解，但是我会尝试一下.我通常使用堆栈/非堆栈而不是数据透视，这更接近您想要的吗?
I'm not sure I understand, but I'll give it a try. I usually use stack/unstack instead of pivot, is this closer to what you want?
df.set_index(['struct_id','resNum','score_type_name']).unstack()
score_value
score_type_name fa_dun fa_dun_dev fa_dun_rot fa_dun_semi omega
struct_id resNum
4294967297 1 2.185618 0.000027 NaN 2.185591 0.064840
2 1.378923 0.028560 1.350362 NaN 0.222345
3 0.020352 0.025507 -0.005156 NaN 0.005106
4 4.218029 0.003712 NaN 4.214317 0.212160
5 3.663050 0.004953 NaN NaN 0.061867
score_type_name p_aa_pp rama ref
struct_id resNum
4294967297 1 NaN NaN -1.191180
2 -0.442467 -0.795161 0.249477
3 -0.096847 0.267443 0.979644
4 -0.462765 -1.403292 -1.960940
5 NaN -0.600053 NaN
我不确定您的枢纽为何不起作用(在我看来有点像它应该，但我可能是错的)，但是如果我放弃，它似乎确实起作用(或至少没有给出错误) 'struct_id'.当然，对于整个数据集来说，这并不是一个有用的解决方案，在该数据集中，"struct_id"具有多个不同的值.
I'm not sure why your pivot isn't working (kinda seems to me like it should, but I could be wrong), but it does seem to work (or at least not give an error) if I leave off 'struct_id'. Of course, that's not really a useful solution for the full dataset where you have more than one different values for 'struct_id'.
df.pivot(columns='score_type_name',values='score_value',index='resNum')
score_type_name fa_dun fa_dun_dev fa_dun_rot fa_dun_semi omega
resNum
1 2.185618 0.000027 NaN 2.185591 0.064840
2 1.378923 0.028560 1.350362 NaN 0.222345
3 0.020352 0.025507 -0.005156 NaN 0.005106
4 4.218029 0.003712 NaN 4.214317 0.212160
5 3.663050 0.004953 NaN NaN 0.061867
score_type_name p_aa_pp rama ref
resNum
1 NaN NaN -1.191180
2 -0.442467 -0.795161 0.249477
3 -0.096847 0.267443 0.979644
4 -0.462765 -1.403292 -1.960940
5 NaN -0.600053 NaN
编辑以添加: reset_index()将从多索引(分层)转换为更扁平的样式.列名称中仍然有一些层次结构，有时，摆脱这些列的最简单方法就是执行df.columns=['var1','var2',...]，尽管如果您进行一些搜索，则还有更复杂的方法.
Edit to add: reset_index() will convert from a multi-index (hierarchical) to a flatter style. There is still some hierarchy in the column names, sometimes the easiest way to get rid of those is just to do df.columns=['var1','var2',...] although there are more sophisticated ways if you do some searching.
df.set_index(['struct_id','resNum','score_type_name']).unstack().reset_index()
struct_id resNum score_value
score_type_name fa_dun fa_dun_dev fa_dun_rot
0 4294967297 1 2.185618 0.000027 NaN
1 4294967297 2 1.378923 0.028560 1.350362
2 4294967297 3 0.020352 0.025507 -0.005156
3 4294967297 4 4.218029 0.003712 NaN
4 4294967297 5 3.663050 0.004953 NaN
这篇关于 pandas :数据透视表和数据透视表之间的区别.为什么只有pivot_table有效?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
