Merge Transform

Column example:

merge col:Column1,Column2 as:'MergedCol'

Output: Merges the contents of Column1 and Column2 in that order into a new column called MergedCol.

Column and string literal example:

merge col:'PID',ProdId with:'-'

Output: Merges the string PID and the values in ProdId together. The string and the value are separated by a dash. Example output value: PID-00123.

merge col:column_ref [with:string_literal_pattern] [as:'new_column_name']

For more information on syntax standards, see Language Documentation Syntax Notes.

Identifies columns or range of columns as source data for the transform. You must specify multiple columns.

To specify multiple columns:

merge col: Prefix,Root,Suffix

Output: Merges the columns Prefix, Root, and Suffix in that order into a new column.

Usage Notes:

Merge Columns transformation: Specifies the delimiter between columns that are merged. If this parameter is not specified, no delimiter is applied.

Replace Text or Pattern transformation: Specifies the replacement value.

merge col: CustId,ProdId with:'-'

Output: Merges the columns CustId and ProdId into a new column with a dash (-) between the source values in the new column.

Usage Notes:

Name of the new column that is being generated. If the as parameter is not specified, a default name is used.

merge col: CustId,ProdId with:'-' as:'PrimaryKey'

Output: Merges the columns CustId and ProdId into a new column with a dash (-) between the source values in the new column. New column is named, PrimaryKey.

Usage Notes:

You have date information stored in multiple columns. You can merge columns together to form a single date value.

Source:

Transformation:

merge col:Month~Year with:'/' as:'Date'

Results:

When you add the transform and move the generated Date column, your dataset should look like the following. Note that the generated column is automatically inferred as Datetime values.

This example illustrates how to clean up data by changing its data type to String, manipulating it using String functions, and then retyping the data to its proper data type.

Functions:

Source:

The following example contains customer ID and Zip code information in two columns. When this data is loaded into the Transformer page, it is initially interpreted as numeric, since it contains all numerals.

The four-digit ZipCode values should have five digits, with a 0 in front.

Transformation:

CustId column: This column needs to be retyped as String values. You can set the column data type to String through the column drop-down, which is rendered as the following transformation:

CustId

String

While the column is now of String type, future transformations might cause it to be re-inferred as Integer values. To protect against this possibility, you might want to add a marker at the front of the string. This marker should be removed prior to execution.

The basic method is to create a new column containing the customer ID marker (C) and then merge this column and the existing CustId column together. It's useful to add such an indicator to the front in case the customer identifier is a numeric value that could be confused with other numeric values. Also, this merge step forces the value to be interpreted as a String value, which is more appropriate for an identifier.

'C',CustId

You can now delete the CustId columns and rename the new column as CustId.

ZipCode column: This column needs to be converted to valid Zip Code values. For ease of use, this column should be of type String:

ZipCode

Zipcode

The transformation below changes the value in the ZipCode column if the length of the value is four in any row. The new value is the original value prepended with the numeral 0:

ZipCode

if(len($col) == 4, merge(['0',$col]), $col)

This column might now be re-typed as Zipcode type.

Results:

Remember to remove the C marker from the CustId column. Select the C value in the CustId column and choose the replace transform. You might need to re-type the cleaned data as String data.