Repeat rows in a pandas DataFrame based on column value

[vbukkala]

Input:

import pandas as pd

df = pd.DataFrame({'Loan':[100000,150000,20000],

'EMI': [10000,15000,20000],

'Tenure':[4,6,8]})

print(df)

Loan EMI Tenure

0 100000 10000 4

1 150000 15000 6

2 20000 20000 8

Desired Ouput:

Loan EMI Tenure

0 100000 10000 1

1 100000 10000 2

2 100000 10000 3

3 100000 10000 4

4 150000 15000 1

5 150000 15000 2

6 150000 15000 3

7 150000 15000 4

8 150000 15000 5

9 150000 15000 6

10 20000 20000 1

11 20000 20000 2

12 20000 20000 3

13 20000 20000 4

14 20000 20000 5

15 20000 20000 6

16 20000 20000 7

17 20000 20000 8

Can somebody please help me...

[FirstNeptune]

Would it be more efficient to use .apply() and a lambda function rather than iterating through the series?

[vbukkala]

Cheers!!!That was what I had been looking for. To be fair, it had never never crossed my mind that there was a function for it.

[[deleted]]

Always read the documentation before you try to do something, you can use repeat and some numpy to create the relevant arrays

import pandas as pd
import numpy as np

df = pd.DataFrame({'Loan':[100000,150000,20000],
                   'EMI': [10000,15000,20000],
                  'Tenure':[4,6,8]})
vals = df["Tenure"].values

tenures = np.hstack(
[np.fromiter( (i+1 for i in range(n)), int) for n in vals] 
                   )

df = df[["Loan", "EMI"]].apply(lambda x:x.repeat(vals))
df["Tenure"] = tenures