datatable.cummin()

datatable.

cummin

(

cols

,

reverse=False

)

sourcetests

Added in version 1.1.0

For each column from cols calculate cumulative minimum. In the presence of by(), the cumulative minimum is computed within each group.

Parameters

cols

FExpr

Input data for cumulative minimum 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 minimums.

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 the cumulative minimum in a single column:

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

	A
	int32
0	2
1	2
2	2
3	-1
4	-1

5 rows × 1 column

Calculate the cumulative minimum from bottom to top:

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

	A
	int32
0	-1
1	-1
2	-1
3	-1
4	0

5 rows × 1 column

Calculate the cumulative minimum in multiple columns:

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

	A	B	C
	int32	void	float64
0	2	NA	5.4
1	2	NA	3
2	2	NA	2.2
3	-1	NA	2.2
4	-1	NA	2.2

5 rows × 3 columns

For a grouped frame calculate the cumulative minimum within each group:

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

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

5 rows × 4 columns