datatable.update()

datatable.

update

(

**kwargs

)

source

Create new or update existing columns within a frame.

This expression is intended to be used at “j” place in DT[i, j] call. It takes an arbitrary number of key/value pairs each describing a column name and the expression for how that column has to be created/updated.

Examples

from datatable import dt, f, by, update DT = dt.Frame([range(5), [4, 3, 9, 11, -1]], names=("A", "B")) DT

	A	B
	int32	int32
0	0	4
1	1	3
2	2	9
3	3	11
4	4	-1

5 rows × 2 columns

Create new columns and update existing columns:

DT[:, update(C = f.A * 2, D = f.B // 3, A = f.A * 4, B = f.B + 1)] DT

	A	B	C	D
	int32	int32	int32	int32
0	0	5	0	1
1	4	4	2	1
2	8	10	4	3
3	12	12	6	3
4	16	0	8	-1

5 rows × 4 columns

Add new column with unpacking; this can be handy for dynamicallly adding columns with dictionary comprehensions, or if the names are not valid python keywords:

DT[:, update(**{"extra column": f.A + f.B + f.C + f.D})] DT

	A	B	C	D	extra column
	int32	int32	int32	int32	int32
0	0	5	0	1	6
1	4	4	2	1	11
2	8	10	4	3	25
3	12	12	6	3	33
4	16	0	8	-1	23

5 rows × 5 columns

You can update a subset of data:

DT[f.A > 10, update(A = f.A * 5)] DT

	A	B	C	D	extra column
	int32	int32	int32	int32	int32
0	0	5	0	1	6
1	4	4	2	1	11
2	8	10	4	3	25
3	60	12	6	3	33
4	80	0	8	-1	23

5 rows × 5 columns

You can also add a new column or update an existing column in a groupby operation, similar to SQL’s window operation, or pandas transform():

df = dt.Frame("""exporter assets liabilities False 5 1 True 10 8 False 3 1 False 24 20 False 40 2 True 12 11""") # Get the ratio for each row per group df[:, update(ratio = dt.sum(f.liabilities) * 100 / dt.sum(f.assets)), by(f.exporter)] df

	exporter	assets	liabilities	ratio
	bool8	int32	int32	float64
0	0	5	1	33.3333
1	1	10	8	86.3636
2	0	3	1	33.3333
3	0	24	20	33.3333
4	0	40	2	33.3333
5	1	12	11	86.3636

6 rows × 4 columns