How to create a timeseries from a dataframe of event durations?

I have a dataframe full of bookings for one room (rows: booking_id, check-in date and check-out date that I want to transform into a timeseries indexed by all year days (index: days of year, feature: booked or not).

I have calculated the duration of the bookings, and reindexed the dataframe daily.Now I need to forward-fill the dataframe, but only a limited number of times: the duration of each booking.

Tried iterating through each row with ffill but it applies to the entire dataframe, not to selected rows.Any idea how I can do that?

Here is my code:

import numpy as npimport pandas as pd#create dataframedata=[[1, '2019-01-01', '2019-01-02', 1],      [2, '2019-01-03', '2019-01-07', 4],       [3, '2019-01-10','2019-01-13', 3]]df = pd.DataFrame(data, columns=['booking_id', 'check-in', 'check-out', 'duration'])#cast dates to datetime formatsdf['check-in'] = pd.to_datetime(df['check-in'])df['check-out'] = pd.to_datetime(df['check-out'])#create timeseries indexed on check-in datedf2 = df.set_index('check-in')#create new index and reindex timeseriesidx = pd.date_range(min(df['check-in']), max(df['check-out']), freq='D')ts = df2.reindex(idx)

I have this:

    booking_id  check-out   duration2019-01-01  1.0     2019-01-02  1.02019-01-02  NaN     NaT     NaN2019-01-03  2.0     2019-01-07  4.02019-01-04  NaN     NaT     NaN2019-01-05  NaN     NaT     NaN2019-01-06  NaN     NaT     NaN2019-01-07  NaN     NaT     NaN2019-01-08  NaN     NaT     NaN2019-01-09  NaN     NaT     NaN2019-01-10  3.0     2019-01-13  3.02019-01-11  NaN     NaT     NaN2019-01-12  NaN     NaT     NaN2019-01-13  NaN     NaT     NaN

I expect to have:

    booking_id  check-out   duration2019-01-01  1.0     2019-01-02  1.02019-01-02  1.0     2019-01-02      1.02019-01-03  2.0     2019-01-07  4.02019-01-04  2.0     2019-01-07  4.02019-01-05  2.0     2019-01-07  4.02019-01-06  2.0     2019-01-07  4.02019-01-07  NaN     NaT     NaN2019-01-08  NaN     NaT     NaN2019-01-09  NaN     NaT     NaN2019-01-10  3.0     2019-01-13  3.02019-01-11  3.0     2019-01-13  3.02019-01-12  3.0     2019-01-13  3.02019-01-13  NaN     NaT     NaN