QST: Rolling on Windows Interval of 'timestamp' should returns same output if all timestamp is same?
- [x] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.

- [x] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).

---

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I've asked on StackOverflow on this [thread](https://stackoverflow.com/questions/68528557/how-come-using-pandass-rolling-with-window-of-timestamp-does-not-return-the-num).

I wish to understand how does rolling works on time intervals given the timestamp is the same.

Let's say, I have 100 records on the same timestamp, for instance '2021-05-11 16:12:57', and my rolling windows period is '3s', shouldn't all the records having the same result as its having the same timestamp?

--- the same as of stackoverflow ---

Input:
```
                     ACCEL_X  ACCEL_Y  ACCEL_Z
DATETIME                                      
2021-05-11 16:12:56      160       32    16392
2021-05-11 16:12:57       20     -192    16548
2021-05-11 16:12:57      128      224    16212
2021-05-11 16:12:57     -148     -132    16624
2021-05-11 16:12:57      -40      204    16132
2021-05-11 16:12:57       72     -132    16536
2021-05-11 16:12:57      220       48    16292
2021-05-11 16:12:57     -132      236    16332
2021-05-11 16:12:57     -232     -132    16628
2021-05-11 16:12:57      192      140    16172
2021-05-11 16:12:57      200      -96    16684
2021-05-11 16:12:57        0       64    16020
2021-05-11 16:12:57     -144      -24    16524
2021-05-11 16:12:57     -160       24    16336
2021-05-11 16:12:57       96       56    16252
2021-05-11 16:12:57       68      -44    16544
2021-05-11 16:12:57       12       76    16308
2021-05-11 16:12:57     -228     -132    16668
2021-05-11 16:12:57       72      -96    16244
2021-05-11 16:12:57       48      -96    16536

df = df.rolling('3s').mean()
df
```

returns
```
                        ACCEL_X    ACCEL_Y       ACCEL_Z
DATETIME                                                
2021-05-11 16:12:56  160.000000  32.000000  16392.000000
2021-05-11 16:12:57   90.000000 -80.000000  16470.000000
2021-05-11 16:12:57  102.666667  21.333333  16384.000000
2021-05-11 16:12:57   40.000000 -17.000000  16444.000000
2021-05-11 16:12:57   24.000000  27.200000  16381.600000
2021-05-11 16:12:57   32.000000   0.666667  16407.333333
2021-05-11 16:12:57   58.857143   7.428571  16390.857143
2021-05-11 16:12:57   35.000000  36.000000  16383.500000
2021-05-11 16:12:57    5.333333  17.333333  16410.666667
2021-05-11 16:12:57   24.000000  29.600000  16386.800000
2021-05-11 16:12:57   40.000000  18.181818  16413.818182
2021-05-11 16:12:57   36.666667  22.000000  16381.000000
2021-05-11 16:12:57   22.769231  18.461538  16392.000000
2021-05-11 16:12:57    9.714286  18.857143  16388.000000
2021-05-11 16:12:57   15.466667  21.333333  16378.933333
2021-05-11 16:12:57   18.750000  17.250000  16389.250000
2021-05-11 16:12:57   18.352941  20.705882  16384.470588
2021-05-11 16:12:57    4.666667  12.222222  16400.222222
2021-05-11 16:12:57    8.210526   6.526316  16392.000000
2021-05-11 16:12:57   10.200000   1.400000  16399.200000
```

