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This example illustrates how to clean up data that has been interpreted as numeric in nature, when it is actually String or a structured string type, such as Gender. This example uses:

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.

CustIdZipCode
40201231234
201212194105
321201294101
13012122020

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:

Transformation Name Change column data type
Parameter: Columns CustId
Parameter: New type 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.

Transformation Name Merge columns
Parameter: Columns '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:

Transformation Name Change column data type
Parameter: Columns ZipCode
Parameter: New type 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:

Transformation Name Edit column with formula
Parameter: Columns ZipCode
Parameter: Formula if(len($col) == 4, merge(['0',$col]), $col)

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

Results:

CustIdZipCode
C402012301234
C201212194105
C321201294101
C130121202020

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.

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