Replicating rows in pandas dataframe by column value and add a new column with repetition index

Question

My question is similar to one asked here. I have a dataframe and I want to repeat each row of the dataframe k number of times. Along with it, I also want to create a column with values 0 to k-1. So

import pandas as pd

df = pd.DataFrame(data={
  'id': ['A', 'B', 'C'],
  'n' : [  1,   2,   3],
  'v' : [ 10,  13,   8]
})

what_i_want = pd.DataFrame(data={
  'id': ['A', 'B', 'B', 'C', 'C', 'C'],
  'n' : [ 1, 2, 2, 3, 3, 3],
  'v' : [ 10,  13, 13, 8, 8, 8],
  'repeat_id': [0, 0, 1, 0, 1, 2]
})

Command below does half of the job. I am looking for pandas way of adding the repeat_id column.

df.loc[df.index.repeat(df.n)]

Answer 1

Use GroupBy.cumcount and copy for avoid SettingWithCopyWarning:

If you modify values in df1 later you will find that the modifications do not propagate back to the original data (df), and that Pandas does warning.

df1 = df.loc[df.index.repeat(df.n)].copy()
df1['repeat_id'] = df1.groupby(level=0).cumcount()
df1 = df1.reset_index(drop=True)
print (df1)
  id  n   v  repeat_id
0  A  1  10          0
1  B  2  13          0
2  B  2  13          1
3  C  3   8          0
4  C  3   8          1
5  C  3   8          2