Functions handling blanks and null values (SPSS Modeler) | IBM Cloud Pak for Data as a Service

Functions handling blanks and null values

Last updated: Jan 17, 2024

Functions handling blanks and null values (SPSS Modeler)

Using CLEM, you can specify that certain values in a field are to be regarded as "blanks," or missing values.

The following functions work with blanks.

Table 1. CLEM blank and null value functions
Function	Result	Description
`@BLANK(FIELD)`	Boolean	Returns true for all records whose values are blank according to the blank-handling rules set in an upstream Type node or Import node (Types tab).
`@LAST_NON_BLANK(FIELD)`	Any	Returns the last value for FIELD that was not blank, as defined in an upstream Import or Type node. If there are no nonblank values for FIELD in the records read so far, $null$ is returned. Note that blank values, also called user-missing values, can be defined separately for each field.
`@NULL(FIELD)`	Boolean	Returns true if the value of FIELD is the system-missing `$null$.` Returns false for all other values, including user-defined blanks. If you want to check for both, use `@BLANK(FIELD)` and`@NULL(FIELD)`.
`undef`	Any	Used generally in CLEM to enter a $null$ value—for example, to fill blank values with nulls in the Filler node.

Blank fields may be "filled in" with the Filler node. In both Filler and Derive nodes (multiple mode only), the special CLEM function @FIELD refers to the current field(s) being examined.