divide data and proper output

divide data and proper output

Tundra.L updated 4 years ago 2 Members · 5 Posts
Python Forum
Tundra.L

Member
November 4, 2020 at 1:24 pm
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For divide credit score to 10 sections, I used code:

list=np.arange(0,1.1,0.1)

for i in list:

q= Loan[‘CR_Score’].quantile(i)

print(q)

the output is series:
```
101.0
331.0
389.0
418.0
440.0
462.0
484.0
507.0
533.0
576.0
846.0
```
but i want to get the output format like :

q0=101

q1=331,…..

q10=846.

How can I make it?
Justin

Administrator
November 4, 2020 at 2:01 pm

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Why not use qcut() function to create a flag variable? It is good for this project.
- Tundra.L
  
  Member
  November 4, 2020 at 2:59 pm
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  thank you.
  
  this is the qcut function I used:
  
  cr_label=range(1,11)
  
  Crdt_seg=pd.qcut(Loan[‘CR_Score’],q=10,labels=cr_label)
  
  Loan=Loan.assign(credit_seg=Crdt_seg.values)
  
  Loan.groupby(‘credit_seg’).agg({‘Client_ID’:’count’})
  
  the output is little different about client numbers in each segments compare with yours. Is there anything wrong in my code?
  - This reply was modified 4 years ago by Tundra.L.
  - This reply was modified 4 years ago by Tundra.L.
  - Justin
    
    Administrator
    November 4, 2020 at 11:14 pm
    
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    No, it’s ok. There may have little difference in creating the bins by SAS Proc Rank and qcut() function. It does NOT matter. It don’t need to be accurate.
    
    Also, please remember to exclude missing, zero or negative scores from analysis.
    - Tundra.L
      
      Member
      November 6, 2020 at 2:17 pm
      
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      got it. thank you

divide data and proper output

Tundra.L

Justin

Tundra.L

Justin

Tundra.L