datatable.cumprod()

datatable.

cumprod

(

cols

,

reverse=False

)

sourcetests

Added in version 1.1.0

For each column from cols calculate cumulative product. The product of the missing values is calculated as one. In the presence of by(), the cumulative product is computed within each group.

Parameters

cols

FExpr

Input data for cumulative product calculation.

reverse

bool

If False, computation is done from top to bottom. If True, it is done from bottom to top.

return

FExpr

f-expression that converts input columns into the columns filled with the respective cumulative products.

except

TypeError

The exception is raised when one of the columns from cols has a non-numeric type.

Examples

Create a sample datatable frame:

from datatable import dt, f, by DT = dt.Frame({"A": [2, None, 5, -1, 0], "B": [None, None, None, None, None], "C": [5.4, 3, 2.2, 4.323, 3], "D": ['a', 'a', 'b', 'b', 'b']})

	A	B	C	D
	int32	void	float64	str32
0	2	NA	5.4	a
1	NA	NA	3	a
2	5	NA	2.2	b
3	-1	NA	4.323	b
4	0	NA	3	b

5 rows × 4 columns

Calculate cumulative product in a single column:

DT[:, dt.cumprod(f.A)]

	A
	int64
0	2
1	2
2	10
3	-10
4	0

5 rows × 1 column

Calculate the cumulative product from bottom to top:

DT[:, dt.cumprod(f.A, reverse=True)]

	A
	int64
0	0
1	0
2	0
3	0
4	0

5 rows × 1 column

Calculate cumulative products in multiple columns:

DT[:, dt.cumprod(f[:-1])]

	A	B	C
	int64	int64	float64
0	2	1	5.4
1	2	1	16.2
2	10	1	35.64
3	-10	1	154.072
4	0	1	462.215

5 rows × 3 columns

For a grouped frame calculate cumulative products within each group:

DT[:, dt.cumprod(f[:]), by('D')]

	D	A	B	C
	str32	int64	int64	float64
0	a	2	1	5.4
1	a	2	1	16.2
2	b	5	1	2.2
3	b	-5	1	9.5106
4	b	0	1	28.5318

5 rows × 4 columns