[Tundra.L]

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If this is what you mean:

normally I used following methods to assign labels for segments

1. customer function to define the segment, the labels will be numeric data

2.cut() or qcut (), the labels will be category data

3. ceil() for special situation, the labels will be numeric data

[Tundra.L]

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My finding is: for both best and worst tier, 0 group’s debtCon/homelep ratio is always higher than 1 group, and the trends is almost the same cross those 10 tiers(decile)

[Tundra.L]

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thank you

[Tundra.L]

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thank you.

non-equal variance means we should use:

equal_var=False

correct?

[Tundra.L]

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can you show us please?

[Tundra.L]

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thank you

[Tundra.L]

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thank you. I switched to loc() function, now it works. the output is like:

0 8535
1 1954
0 8496
1 1749
.......

• This reply was modified 3 years, 5 months ago by  Tundra.L.

[Tundra.L]

thank you

[Tundra.L]

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I misunderstand then. the TN is supposed be 1 & 1. I thought it is 1 &(1,0), that’s why

thank you

[Tundra.L]

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got it. thank you

[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 3 years, 6 months ago by  Tundra.L.
• This reply was modified 3 years, 6 months ago by  Tundra.L.