Pandas expanding a dataframe length but populate each row incrementally based on column

Question

I'm working with a dataframe that looks like this:

    frame   requests
0   0   214388438.0
1   1   194980303.0
2   2   179475934.0
3   3   165196540.0
4   4   154815540.0
5   5   123650671.0
6   6   119089045.0

The thing is I want to add each of the value found on the requests column incrementally. Say frame 0 should have the first value, frame 1 should have the previous value and the one that comes after followed by 0. I want the dataframe to look something like this:

        frame   requests
    0   0   214388438.0
    1   0             0
    2   0             0
    3   0             0
   ....................
   48   1   214388438.0
   49   1   194980303.0
   50   1             0
   ....................
   ..   2   214388438.0
   ..   2   194980303.0
   ..   2   179475934.0
   ..   2             0

Eventually, on the last value for column frame all rows would be populated by the value on the requests, no more 0s.

   ....................
   ..   47   214388438.0
   ..   47   194980303.0
   ..   47   179475934.0
   ..   47   165196540.0
   ..   47   154815540.0
   ..   47   123650671.0
   .....................

Answer 1

Assuming df as input, you can use numpy to reshape and create a new DataFrame:

import numpy as np

a = df['requests'].to_numpy()

df2 = (pd
 .DataFrame(np.tril(np.tile(a, (len(a), 1))), index=df['frame'])
 .stack()
 .droplevel(1)
 .reset_index(name='requests')
)

NB. You can also use df['requests'] directly instead of a , the conversion to array will be done automatically.

output:

    frame     requests
0       0  214388438.0
1       0          0.0
2       0          0.0
3       0          0.0
4       0          0.0
5       0          0.0
6       0          0.0
7       1  214388438.0
8       1  194980303.0
9       1          0.0
10      1          0.0
11      1          0.0
12      1          0.0
13      1          0.0
14      2  214388438.0
15      2  194980303.0
16      2  179475934.0
17      2          0.0
18      2          0.0
19      2          0.0
20      2          0.0
21      3  214388438.0
22      3  194980303.0
23      3  179475934.0
24      3  165196540.0
25      3          0.0
26      3          0.0
27      3          0.0
28      4  214388438.0
29      4  194980303.0
30      4  179475934.0
31      4  165196540.0
32      4  154815540.0
33      4          0.0
34      4          0.0
35      5  214388438.0
36      5  194980303.0
37      5  179475934.0
38      5  165196540.0
39      5  154815540.0
40      5  123650671.0
41      5          0.0
42      6  214388438.0
43      6  194980303.0
44      6  179475934.0
45      6  165196540.0
46      6  154815540.0
47      6  123650671.0
48      6  119089045.0