datatable.shift()

datatable.

shift

(

cols

,

n=1

)

sourcetests

Shift columns’ data forward or backwards. This function is group-aware, i.e. in the presence of a groupby it will shift data separately within each group.

Parameters

cols

FExpr

Input data to be shifted.

n

int

The shift amount. For positive n, the data are shifted forward, i.e. a “lag” column is created. For negative n, the data are shifted backwards creating a “lead” column.

return

FExpr

f-expression that shifts input data by n rows. The resulting data will have the same number of rows as cols. Depending on n, i.e. positive/negative, n observations in the beginning/end will become missing and n observations at the end/beginning will be discarded.

Examples

from datatable import dt, f, by DT = dt.Frame({"object": [1, 1, 1, 2, 2], "period": [1, 2, 4, 4, 23], "value": [24, 67, 89, 5, 23]}) DT

	object	period	value
	int32	int32	int32
0	1	1	24
1	1	2	67
2	1	4	89
3	2	4	5
4	2	23	23

5 rows × 3 columns

Shift forward by creating a “lag” column:

DT[:, dt.shift(f.period, n = 3)]

	period
	int32
0	NA
1	NA
2	NA
3	1
4	2

5 rows × 1 column

Shift backwards by creating “lead” columns:

DT[:, dt.shift(f[:], n = -3)]

	object	period	value
	int32	int32	int32
0	2	4	5
1	2	23	23
2	NA	NA	NA
3	NA	NA	NA
4	NA	NA	NA

5 rows × 3 columns

Shift within groups, i.e. in the presence of by():

DT[:, f[:].extend({"prev_value": dt.shift(f.value)}), by("object")]

	object	period	value	prev_value
	int32	int32	int32	int32
0	1	1	24	NA
1	1	2	67	24
2	1	4	89	67
3	2	4	5	NA
4	2	23	23	5

5 rows × 4 columns