根据数据创建时间序列

Question

I have a dataframe which contains information on defaults within a loan portfolio and the time from origination from which it occurred. Each 'observation' is a pair representing time t in days, and amount of loan default:

df['time_to_default']  #  Time from origination to default
df['default_amnt']     #  The loan amount defaulted

I would like to create a series which represents the cumulative amount of defaults for any given time t. (Assume that time_to_default is evenly divisible by t). I cannot figure out how to create a new dataframe element, assign the initial value to 0 and then iterate through the series....

Answer 1

It sounds like you need to use groupby for this together with cumsum since you want a running total:

cum_defaults = df.groupby('time_to_default').default_amnt.sum().cumsum()

You then need to reindex this new series to fill in any missing days:

cum_defaults = cum_defaults.reindex(index=range(min(cum_defaults.index),
                                                max(cum_defaults.index) + 1), 
                                    method='ffill')

With some example data:

df = pd.DataFrame({'time_to_default': [1, 3, 3, 6], 
                   'default_amnt': [10, 20, 30, 40]})
>>> cum_defaults
time_to_default
1     10
2     10
3     60
4     60
5     60
6    100
Name: default_amnt, dtype: int64