[Python] Parse dataframe into list of subinterval dataframes for processing

I have a dataframe, esu_tos:

Time TickType Price Size
2023-08-13 15:10:46.166 1 4487.25 1
2023-08-13 15:10:47.375 1 4487 1
2023-08-13 15:10:54.656 1 4487.25 2
2023-08-13 15:10:57.627 1 4487 1
2023-08-13 15:10:57.628 1 4487 1
2023-08-13 15:10:57.628 1 4487 1
2023-08-13 15:11:00.759 1 4487.25 1
2023-08-13 15:11:00.759 1 4487 3
2023-08-13 15:11:01.415 1 4487 3
2023-08-13 15:11:01.416 1 4487 1


That I'd like to parse into sub-intervals like below. Is there a better way to do this aside from the loop?

interval = '5s' # 1min / 30s / 10s / 5s
st = datetime(2023,8,13,15,10)
end = datetime(2023,8,14,14,1)

rng = pd.date_range(st, end, freq = interval, inclusive='both')


d = []
d_t = []
for i, k in enumerate(rng):
try:
a = esu_tos[(esu_tos.Time >= k) & (esu_tos.Time < rng[i+1])]

except:
a = esu_tos[(esu_tos.Time >= k) & (esu_tos.Time <= end)]

d.append(a)
d_t.append(k)


Looking specifically to return a list or other object which will allow me to access the data aggregated to each interval -- not simply as pd.Grouper or groupby object.

For instance, the included code creates a list of dataframes, which I'm subsequently able to access process individually.

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