Pandas: need to create dataframe for weekly search per event occurrence - pandas

Question

If I have this events dataframe df_e below:
|------|------------|-------|
| group| event date | count |
| x123 | 2016-01-06 | 1 |
| | 2016-01-08 | 10 |
| | 2016-02-15 | 9 |
| | 2016-05-22 | 6 |
| | 2016-05-29 | 2 |
| | 2016-05-31 | 6 |
| | 2016-12-29 | 1 |
| x124 | 2016-01-01 | 1 |
...
and also know the t0 which is the beginning of time (let's say for x123 it's 2016-01-01) and tN which is the end of experiment from another dataframe df_s (2017-05-25), then how can I create the dataframe df_new which should like this
|------|------------|---------------|--------|
| group| obs. weekly| lifetime, week| status |
| x123 | 2016-01-01 | 1 | 1 |
| | 2016-01-08 | 0 | 0 |
| | 2016-01-15 | 0 | 0 |
| | 2016-01-22 | 1 | 1 |
| | 2016-01-29 | 2 | 1 |
...
| | 2017-05-18 | 1 | 1 |
| | 2017-05-25 | 1 | 1 |
...
| x124 | 2017-05-18 | 1 | 1 |
| x124 | 2017-05-25 | 1 | 1 |
Explanation: take t0 and generate rows until tN per week period. For each row R, search with that group if the event date falls within R, if True, then count how long in weeks it lives there, also set status = 1 as alive, otherwise set lifetime, status columns for this R as 0, e.g. dead.
Questions:
1) How to generate dataframes per group given t0 and tN values, e.g. generate [group, obs. weekly, lifetime, status] columns for (tN - t0) / week rows?
2) How to accomplish the construction of such df_new dataframe explained above?
I can begin with this so far =)
import pandas as pd
# 1. generate dataframes per group to get the boundary within `t0` and `tN` from df_s dataframe, where each dataframe has "group, obs, lifetime, status" columns X (tN - t0 / week) rows filled with 0 values.
df_all = pd.concat([df_group1, df_group2])
def do_that(R):
found_event_row = df_e.iloc[[R.group]]
# check if found_event_row['date'] falls into R['obs'] week
# if True, then found how long it's there
df_new = df_all.apply(do_that)

Answer 1

I'm not really sure if I get you but group one is not related to group two, right? if that's the case I think what you want is something like this:
import pandas as pd
df_group1 = df_group1.set_index('event date')
df_group1.index = pd.to_datetime(df_group1.index) #convert the index to datetime so you can 'resample'
df_group1['lifetime, week'] = df_group1.resample('1W').apply(lamda x: yourfuncion(x))
df_group1 = df_group1.reset_index()
df_group1['status']= df_group1.apply(lambda x: 1 if x['lifetime, week']>0 else 0)
#do the same with group2 and concat to create df_all
I'm not sure how you get 'lifetime, week' but all that's left is creating the function that generates it.