Pandas Group/Merge Dataframe by Non-Periodic Series - pandas

Question

How do I group one DataFrame by another possibly-non-periodic Series? Mock-up below:
This is the DataFrame to be split:
i = pd.date_range(end="today", periods=20, freq="d").normalize()
v = np.random.randint(0,100,size=len(i))
d = pd.DataFrame({"value": v}, index=i)
>>> d
value
2021-02-06 48
2021-02-07 1
2021-02-08 86
2021-02-09 82
2021-02-10 40
2021-02-11 22
2021-02-12 63
2021-02-13 37
2021-02-14 41
2021-02-15 57
2021-02-16 30
2021-02-17 69
2021-02-18 63
2021-02-19 27
2021-02-20 23
2021-02-21 46
2021-02-22 66
2021-02-23 10
2021-02-24 91
2021-02-25 43
This is the splitting criteria, grouping by the Series dates. A group consists of any ordered dataframe value v such that {v} intersects [s,s+1) - but as with resampling it would be nice to control the inclusion parameters.
s = pd.date_range(start="2019-10-14", freq="2W", periods=52).to_series()
s = s.drop(np.random.choice(s.index, 10, replace=False))
s = s.reset_index(drop=True)
>>> s[25:29]
25 2021-01-24
26 2021-02-07
27 2021-02-21
28 2021-03-07
dtype: datetime64[ns]
And this is the example output... or something like it. Index is taken from the series rather than the dataframe.
>>> ???.sum()
value
...
2021-01-24 47
2021-02-07 768
2021-02-21 334
...
Internally the groups would have this structure:
...
2021-01-10
sum: 0
2021-01-24
2021-02-06 47
sum: 47
2021-02-07
2021-02-07 52
2021-02-08 56
2021-02-09 21
2021-02-10 39
2021-02-11 86
2021-02-12 30
2021-02-13 20
2021-02-14 76
2021-02-15 91
2021-02-16 70
2021-02-17 34
2021-02-18 73
2021-02-19 41
2021-02-20 79
sum: 768
2021-02-21
2021-02-21 90
2021-02-22 75
2021-02-23 12
2021-02-24 70
2021-02-25 87
sum: 334
2021-03-07
sum: 0
...

Answer 1

Looks like you can do:
bucket = pd.cut(d.index, bins=s, label=s[:-1], right=False)
d.groupby(bucket).sum()