融化具有多个变量名称和多个值名称的 pandas 数据框 [英] Melting pandas data frame with multiple variable names and multiple value names

问题描述

如何使用多个变量名称和值来融合 Pandas 数据框?我有以下数据框在 for 循环中改变其形状.在 for 循环迭代之一中，它看起来像这样:

How can I melt a pandas data frame using multiple variable names and values? I have the following data frame that changes its shape in a for loop. In one of the for loop iterations, it looks like this:

ID  Cat    Class_A   Class_B     Prob_A     Prob_B
1   Veg      1        2          0.9         0.1
2   Veg      1        2          0.8         0.2
3   Meat     1        2          0.6         0.4
4   Meat     1        2          0.3         0.7
5   Veg      1        2          0.2         0.8

我需要把它融化成这样:

I need to melt it in such a way that it looks like this:

ID  Cat    Class     Prob    
1   Veg      1       0.9       
1   Veg      2       0.1
2   Veg      1       0.8        
2   Veg      2       0.2
3   Meat     1       0.6         
3   Meat     2       0.4
4   Meat     1       0.3         
4   Meat     2       0.7
5   Veg      1       0.2         
5   Veg      2       0.8

在 for 循环期间，数据框将包含不同数量的类及其概率.这就是为什么我正在寻找一种适用于我所有 for 循环迭代的通用方法.我看到了这个问题和这个但他们没有很有帮助！

During the for loop the data frame will contain different number of classes with their probabilities. That is why I am looking for a general approach that is applicable in all my for loop iterations. I saw this question and this but they were not helpful!

推荐答案

您需要 lreshape 由 dict 指定类别:

d = {'Class':['Class_A', 'Class_B'], 'Prob':['Prob_A','Prob_B']}
df = pd.lreshape(df,d)
print (df)
    Cat  ID  Class  Prob
0   Veg   1      1   0.9
1   Veg   2      1   0.8
2  Meat   3      1   0.6
3  Meat   4      1   0.3
4   Veg   5      1   0.2
5   Veg   1      2   0.1
6   Veg   2      2   0.2
7  Meat   3      2   0.4
8  Meat   4      2   0.7
9   Veg   5      2   0.8

更动态的解决方案:

Class = [col for col in df.columns if col.startswith('Class')]
Prob = [col for col in df.columns if col.startswith('Prob')]
df = pd.lreshape(df, {'Class':Class, 'Prob':Prob})
print (df)
    Cat  ID  Class  Prob
0   Veg   1      1   0.9
1   Veg   2      1   0.8
2  Meat   3      1   0.6
3  Meat   4      1   0.3
4   Veg   5      1   0.2
5   Veg   1      2   0.1
6   Veg   2      2   0.2
7  Meat   3      2   0.4
8  Meat   4      2   0.7
9   Veg   5      2   0.8

lreshape 现在没有记录，但将来可能会被删除 (与 pd.wide_to_long 也).

可能的解决方案是将所有 3 个功能合并为一个 - 也许 melt，但现在尚未实现.也许在一些新版本的熊猫中.然后我的答案会更新.

Possible solution is merging all 3 functions to one - maybe melt, but now it is not implementated. Maybe in some new version of pandas. Then my answer will be updated.

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