How to sample a python df on daily rate when it is greater than 500 yrs

Question

I need to sample a dataframe that has a date range of 100 years at a daily rate because I want to get yearly totals (so I thought resample at daily rate then sum the yearly totals).

I tried

d0=start_date
# set date to model start date
d=d0
ind =Time_data2['datetime']
df_out=pd.DataFrame(index=range((max(ind)-d0).days),columns= 
['datetime','year','value'])

for i in range((max(ind)-d0).days):             # for every day in the total number of days in the simulation 
d = d0 + datetime.timedelta(days=i)         # get a particular day (= start_date + timedelta)
df_out.loc[i,'datetime']=d                  # assign datetime for each day
df_out.loc[i,'year']=d.year                 # assign year for each day
# Assign value based on the first value in the raw timeseries that proceeds the day being filled, this is equivilent to a backfill with the pandas resample
for t in model_flow_ts.index:
    dt = t-d               # calcualtes a timedelta between each index value in model_flow_ts and the particular day in the simulation
    if dt.days < 0:        
        continue
    else:
        v = model_flow_ts.loc[t]  # get the value
        break
df_out.loc[i,'value']=v
if i/50000==int(i/50000):
    print(i)

But it takes a really long time because there are so many days to sample...

Does anyone have any suggestions on how to speed it up?

cheers