Melting pandas data frame with multiple variable names and multiple value names

Question

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

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

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!

Answer 1

You need lreshape by dict for specify categories:

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

More dynamic solution:

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

EDIT:

lreshape is now undocumented, but is possible in future will by removed (with pd.wide_to_long too).

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.

Answer 2

Or you can try this by using str.contain and pd.concat

DF1=df2.loc[:,df2.columns.str.contains('_A|Cat|ID')]
name=['ID','Cat','Class','Prob']
DF1.columns=name
DF2=df2.loc[:,df2.columns.str.contains('_B|Cat|ID')]
DF2.columns=name
pd.concat([DF1,DF2],axis=0)

Out[354]: 
   ID   Cat  Class  Prob
0   1   Veg      1   0.9
1   2   Veg      1   0.8
2   3  Meat      1   0.6
3   4  Meat      1   0.3
4   5   Veg      1   0.2
0   1   Veg      2   0.1
1   2   Veg      2   0.2
2   3  Meat      2   0.4
3   4  Meat      2   0.7
4   5   Veg      2   0.8