Lag
Lag shifts your features on a partition index, creating a lookback feature offset by an amount. Lag supports generating multiple lags in one transform by generating each unique combination of columns and amounts from your inputs.
Name | Type | Description | Is Optional |
---|---|---|---|
columns | column_list | names of column(s) you want to lag | |
amounts | int_list | Magnitude of amounts you want to use for the lag. Positive values result in a historical offset; negative amounts result in forward-looking offset. | |
partition | column_list | name of column(s) to partition by for the lag | True |
order_by | column_list | name of column(s) to order by in the final data set | True |
ds = rasgo.get.dataset(id)
ds2 = ds.lag(columns=['OPEN', 'CLOSE'], amounts=[1,2,3,7], order_by=['DATE, 'TICKER'], partition=['TICKER'])
ds2.preview()
Last modified 1yr ago