datatable.Frame.replace()

datatable.Frame.

replace

(

replace_what,

replace_with

)

source

Replace given value(s) replace_what with replace_with in the entire Frame.

For each replace value, this method operates only on columns of types appropriate for that value. For example, if replace_what is a list [-1, math.inf, None, "??"], then the value -1 will be replaced in integer columns only, math.inf only in real columns, None in columns of all types, and finally "??" only in string columns.

The replacement value must match the type of the target being replaced, otherwise an exception will be thrown. That is, a bool must be replaced with a bool, an int with an int, a float with a float, and a string with a string. The None value (representing NA) matches any column type, and therefore can be used as either replacement target, or replace value for any column. In particular, the following is valid: DT.replace(None, [-1, -1.0, ""]). This will replace NA values in int columns with -1, in real columns with -1.0, and in string columns with an empty string.

The replace operation never causes a column to change its logical type. Thus, an integer column will remain integer, string column remain string, etc. However, replacing may cause a column to change its stype, provided that ltype remains constant. For example, replacing 0 with -999 within an int8 column will cause that column to be converted into the int32 stype.

Parameters

replace_what

None | bool | int | float | list | dict

Value(s) to search for and replace.

replace_with

single value | list

The replacement value(s). If replace_what is a single value, then this must be a single value too. If replace_what is a list, then this could be either a single value, or a list of the same length. If replace_what is a dict, then this value should not be passed.

return

None

Nothing is returned, the replacement is performed in-place.

Examples

df = dt.Frame([1, 2, 3] * 3) df.replace(1, -1) df

	C0
	int32
0	-1
1	2
2	3
3	-1
4	2
5	3
6	-1
7	2
8	3

9 rows × 1 column

df.replace({-1: 100, 2: 200, "foo": None}) df

	C0
	int32
0	100
1	200
2	3
3	100
4	200
5	3
6	100
7	200
8	3

9 rows × 1 column