How calculate an average value of the most recent events across groups in pandas dataframe?

I have a pandas dataframe with events (timestamp, value, company id etc).

EXAMPLE:

timestamp	value	name	nusers
0	2023-06-01 10:46:11	-1	A	1000
1	2023-06-01 11:12:12	1	A	1000
2	2023-06-01 15:52:44	0	A	1000
3	2023-06-01 18:24:15	0	A	1000
4	2023-06-01 19:19:58	1	A	1000
0	2023-06-01 07:00:41	0	B	2000
1	2023-06-01 09:44:46	-1	B	2000
2	2023-06-01 15:06:21	1	B	2000
3	2023-06-01 15:32:35	0	B	2000
4	2023-06-01 21:55:05	-1	B	2000
0	2023-06-01 08:20:33	0	C	3000
1	2023-06-01 15:02:17	-1	C	3000
2	2023-06-01 17:09:25	1	C	3000
3	2023-06-01 21:51:31	0	C	3000
4	2023-06-01 22:12:48	0	C	3000

and for each event I need to calculate an average value of the most recent events across companies at that moment in time. Of course, the most straightforward way would be just loop through all rows, take the most recent events for each company less than the current tie stamp, and calculate an average.

So for the dataframe above the 'naive' code that works looks like that:

res=[]
for index, row in df.iterrows():
    recent=df[df.timestamp<=row.timestamp]
    latest_values=recent.groupby('name').last()
    res.append(dict(timestamp=row.timestamp, value=latest_values.value.mean()))

aggregated_df=pd.DataFrame(res)
aggregated_df.sort_values('timestamp', inplace=True)
aggregated_df

which results in what I need:

	timestamp	value
5	2023-06-01 07:00:41	0.000000
10	2023-06-01 08:20:33	0.000000
6	2023-06-01 09:44:46	-0.500000
0	2023-06-01 10:46:11	-0.666667
1	2023-06-01 11:12:12	0.000000
11	2023-06-01 15:02:17	-0.333333
7	2023-06-01 15:06:21	0.333333
8	2023-06-01 15:32:35	0.000000
2	2023-06-01 15:52:44	-0.333333
12	2023-06-01 17:09:25	0.333333
3	2023-06-01 18:24:15	0.333333
4	2023-06-01 19:19:58	0.666667
13	2023-06-01 21:51:31	0.333333
9	2023-06-01 21:55:05	0.000000
14	2023-06-01 22:12:48	0.000000

But I wonder if there is a more pandas-like way of having the same result.

The following works.

df.sort_values("timestamp").set_index(["timestamp", "name"])\
    ["value"].unstack().ffill().mean(axis=1)

#Out[]: 
#timestamp
#2023-06-01 07:00:41    0.000000
#2023-06-01 08:20:33    0.000000
#2023-06-01 09:44:46   -0.500000
#2023-06-01 10:46:11   -0.666667
#2023-06-01 11:12:12    0.000000
#2023-06-01 15:02:17   -0.333333
#2023-06-01 15:06:21    0.333333
#2023-06-01 15:32:35    0.000000
#2023-06-01 15:52:44   -0.333333
#2023-06-01 17:09:25    0.333333
#2023-06-01 18:24:15    0.333333
#2023-06-01 19:19:58    0.666667
#2023-06-01 21:51:31    0.333333
#2023-06-01 21:55:05    0.000000
#2023-06-01 22:12:48    0.000000
#dtype: float64

Components:

1. Sort values so that in chronological order
2. Set index for timestamp and name
3. Take the value column only
4. Then unstack (so names are different columns)
5. Forward fill so last value in time for each name for each timestamp
6. Take the mean for each row (timestamp)