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.

Parameters

NameTypeDescriptionIs 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

Example

ds = rasgo.get.dataset(id)

ds2 = ds.lag(columns=['OPEN', 'CLOSE'], amounts=[1,2,3,7], order_by=['DATE, 'TICKER'], partition=['TICKER'])
ds2.preview()

Source Code

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