How do I solve this kind of problem through pandas.cut()? - pandas

Question

I have my data as
data = pd.DataFrame({'A':[3,50,50,60],'B':[49,5,37,59],'C':[15,34,43,6],'D':[35,39,10,25]})
If I use cut this way
p = ['A','S','T','U','V','C','Z']
bins = [0,30,35,40,45,50,55,60]
data['A*'] = pd.cut(data.A,bins,labels=p)
print(data)
I get
A B C D A*
0 3 49 15 35 A
1 50 5 34 39 V
2 50 37 43 10 V
3 60 59 6 25 Z
How would I cut it to get
A B C D A*
0 3 49 15 35 3A
1 50 5 34 39 50V
2 50 37 43 10 50V
3 60 59 6 25 60Z
I tried this but doesn't work
for x in data.A:
p = [str(x)+'A',str(x)+'S',str(x)+'T',str(x)+'U',str(x)+'V',str(x)+'C',str(x)+'Z']
bins = [0,30,35,40,45,50,55,60]
It gives me this
A B C D A*
0 3 49 15 35 60A
1 50 5 34 39 60V
2 50 37 43 10 60V
3 60 59 6 25 60Z

Answer 1

Convert column A to strings and categoricals from pd.cut too and join together:
p = ['A','S','T','U','V','C','Z']
bins = [0,30,35,40,45,50,55,60]
data['A*'] = data.A.astype(str) + pd.cut(data.A,bins,labels=p).astype(str)
print(data)
A B C D A*
0 3 49 15 35 3A
1 50 5 34 39 50V
2 50 37 43 10 50V
3 60 59 6 25 60Z
EDIT:
For processing all columns is possible use DataFrame.apply:
data = data.apply(lambda x: x.astype(str) + pd.cut(x,bins,labels=p).astype(str))
print(data)
A B C D
0 3A 49V 15A 35S
1 50V 5A 34S 39T
2 50V 37T 43U 10A
3 60Z 59Z 6A 25A