Whether data is missing, mismatched, or simply wrong, you can use a variety of methods in the

Replacement methods

In the Transformer page, you can use the following methods to replace values: 

Replace by selection

When you select data in the data grid, the replacement suggestions are pre-specified for you, including a number of variants available in the suggestion card. 

Notes:

• Suggestions are typically conservative in the scope of their changes. Case-sensitive searches and matching of the first occurrence only are the default settings.
• Order of listing of suggestions in a suggestion card:
  • Pattern-based replacements are listed first. These replacements use 

    D s lang
    item patterns

    , instead of regular expressions. Regular expressions can be more difficult to control.
  • Literal value replacements are listed below the pattern-based ones. 

For more information, see Overview of Predictive Transformation.

Mask data

For privacy reasons or for sensitivity reasons, you may wish to mask sensitive data in one or more columns with fixed strings.

Delete whole column(s)

If you need to remove the data in an entire column, the easiest method is to delete a column. Select one or more columns and then select Delete from the column drop-down. See Remove Data.

Masking all values

You can use a transformation like the following to replace all values in a column with a simple string. In this case, the value #REDACTED# has been inserted in place of all values in the column. 

Partial masking of values

Suppose you wish to partially mask data in a column. In the following example, data for the AcctNum column is masked, except for the last four characters (digits):

Mask multiple columns based on data type

You can use the following type of transformation to hide data based on data type. In this example, the values in all columns with Social Security Number (SSN) are replaced with a masking value: XXX-XX-XXXX:

Replace with values from another column

Replace whole column

You can do simple replacements of data from one column into another with transformations like the following. In this example, the values of colB are replaced with the values of colA with 0.15 added to them:

Replace partial values from another column

You can use the MERGE function to blend full or partial sets of columns into a new column. In the following example, the newBrandId value is concatenated with the product code in the ProdId column to create a new product identifier:

Replace between positions

You can perform replacements based on character positions that you specify as part of the transformation.

• The beginning character value is specified as a number from 0, which starts on the left.
• The ending character value must be equal to or greater than the beginning character value.

In the following example, the Whse_Name column values are prepended with the value old-. 

Search and replace text or pattern

You can search and replace content in your dataset based on literals or patterns. In the following example, the value ##CLT_NAME## is replaced with Our Customer, Inc. across all columns in the dataset:

Replace missing values

Replace missing with zeroes

For numeric data, you may choose to replace values that are missing in a column with zeros. The following transformation sets missing values in the Qty and DiscountPct columns of Decimal data type to 0:

Replace missing with average values

One of the problems with the above method is that any statistical computations applied to the column are now affected by the zeroing of the missing values. For example, the computation for the AVERAGE function does not factor in missing values into the count of rows, which result in skewing of values for your purposes. 

The following example creates a new column from the DiscountPct column in which empty values are inserted as the average of the values in the source column:

In this manner, the new column can be used for some statistical modeling, while preserving the original values in the original column.

Replace mismatched values

You can perform replacements based on the values in a column that are mismatched against a specified type. 

In the following example, Datetime values that do not match the yyyy*mm*dd, where the asterisk (*) is a wildcard value.

Rename columns

For more information, see Rename Columns.