Change value of a slice in pandas depending on the number of rows in the slice

Question

I have a pandas dataframe that looks like this

import pandas as pd

df = pd.DataFrame({'Timestamp': ['1642847484', '1642847484', '1642847484', '1642847484', '1642847487', '1642847487','1642847487','1642847487','1642847487','1642847487','1642847487','1642847487', '1642847489', '1642847489', '1642847489'],
                   'value': [11, 10, 14, 20, 3, 2, 9, 48, 5, 20, 12, 20, 56, 12, 8]})

The data is collected in batches which results in multiple lines having the same timestamp . I need to index the dataframe with time and to do so the indexes must have unique values.

The problem as you can see is:

• The timestamp step is varriant
• The number of rows for each timestep is varriant

The approach I tried is

1. Multiply timestamp by 1000 to get microseconds
2. calculate the time beween timestep i and the next timestep j delta = j-i
3. count the number of rows n between i and j
4. for each row between i and j add ( 1/n+1 * rank) seconds

expected output:

        Timestamp  value
0   1642847484000     11
1   1642847484750     10
2   1642847485500     14
3   1642847484000     20
4   1642847487000      3
5   1642847487250      2
6   1642847487500      9
7   1642847487750     48
8   1642847488000      5
9   1642847488250     20
10  1642847488500     12
11  1642847488750     20
12  1642847489000     56
13  1642847489333     12
14  1642847489666      8
15  1642847490000      4

But I couldn't find a way to that efficiently, I used loops but I have 15M+ rows

Is there a simpler way to do it ? Thank you