datatable.ifelse()

datatable.

ifelse

(

condition1,

value1,

condition2,

value2,

...,

default

)

sourcetests

Added in version 0.11.0

An expression that chooses its value based on one or more conditions.

This is roughly equivalent to the following Python code:

result = value1 if condition1 else \ value2 if condition2 else \ ... else \ default

For every row this function evaluates the smallest number of expressions necessary to get the result. Thus, it evaluates condition1, condition2, and so on until it finds the first condition that evaluates to True. It then computes and returns the corresponding value. If all conditions evaluate to False, then the default value is computed and returned.

Also, if any of the conditions produces NA then the result of the expression also becomes NA without evaluating any further conditions or values.

Parameters

condition1

,

condition2

,

...

FExpr[bool]

Expressions each producing a single boolean column. These conditions will be evaluated in order until we find the one equal to True.

value1

,

value2

,

...

FExpr

Values that will be used when the corresponding condition evaluates to True. These must be single columns.

default

FExpr

Value that will be used when all conditions evaluate to False. This must be a single column.

return

FExpr

The resulting expression is a single column whose stype is the common stype for all value1, …, default columns.

Notes

Changed in version 1.0.0

Earlier this function accepted a single condition only.

Examples

Single condition

Task: Create a new column Colour, where if Set is 'Z' then the value should be 'Green', else 'Red':

from datatable import dt, f, by, ifelse, update df = dt.Frame("""Type Set A Z B Z B X C Y""") df[:, update(Colour = ifelse(f.Set == "Z", # condition "Green", # if condition is True "Red")) # if condition is False ] df

	Type	Set	Colour
	str32	str32	str32
0	A	Z	Green
1	B	Z	Green
2	B	X	Red
3	C	Y	Red

4 rows × 3 columns

Multiple conditions

Task: Create new column value whose value is taken from columns a, b, or c – whichever is nonzero first:

df = dt.Frame({"a": [0,0,1,2], "b": [0,3,4,5], "c": [6,7,8,9]}) df

	a	b	c
	int32	int32	int32
0	0	0	6
1	0	3	7
2	1	4	8
3	2	5	9

4 rows × 3 columns

df['value'] = ifelse(f.a > 0, f.a, # first condition and result f.b > 0, f.b, # second condition and result f.c) # default if no condition is True df

	a	b	c	value
	int32	int32	int32	int32
0	0	0	6	6
1	0	3	7	3
2	1	4	8	1
3	2	5	9	2

4 rows × 4 columns