divide data and proper output

[Tundra.L]

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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]

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Why not use qcut() function to create a flag variable? It is good for this project.

[Tundra.L]

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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]

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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]

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got it. thank you