Unlike other types of data, text data has very few restrictions on the kinds of values that appear in a cell. In the application, this data is typically inferred as String data type. As a result, finding string values that mean the same thing can be a challenge, as minor differences in their content or structure can invalidate a match.

This section provides some methods for matching text values.

Example Data

In the following example, you can see that there are minor differences between the String values in each row of the dataset. These differences are captured in the Description column.

My StringBase string: 'My String'
My String extraBase string + ' extra'
 My StringA space in front of base string
My String A space after base string
MyStringNo space between the two words of base string
My  StringTwo spaces between the two words of base string
My StringBase string + a tab character
My String
Base string + a return character
My String
Base string + a newline character

When this data is imported, it looks like the following, after minor cleanup:

Example data after import


To normalize these text values, you can use some of the techniques listed on this page to match the problematic string values in this dataset and correct them, as needed.

Finer-Grained Controls

For closer control over string matching and cleanup, you can apply individual transforms to your column of string data. The sections below outline a number of techniques for identifying matches and cleaning up your data.

Trim strings

NOTE: Before you begin matching data, you should perform a TRIM transform to remove whitespace at the beginning and end of the string, unless the whitespace is significant to the meaning and usage of the string data. 

When transforming strings, a key step is to trim off the whitespace at the beginning and ending of the string. For the above dataset, you can use the following command to remove these whitespaces:

The above transform uses the following special values, which are available for some transforms like set:

Special ValueDescription
*For the Columns textbox, you can use this wildcard to reference all columns in the dataset.
$colWhen multiple columns are referenced in a transform, this special value allows you to reference the source column in a replacement value.

The previewed data looks like the following, in which five strings are modified and now match the base string:

Trim data to improve matches

To remove all whitespace, including spaces in between, you can use the REMOVEWHITESPACE function. See REMOVEWHITESPACE Function.

Use missing or mismatched value presets

The platform language, , provides presets to identify missing or mismatched values in a selection of data.

Tip: In a column's histogram, click the missing or mismatched categories to trigger a set of suggestions.

Missing values preset: The following transform replaces missing URL values with the text string http://www.example.com. The preset ISMISSING([Primary_WebSite_or_URL]) identifies the rows missing data in the specified column:

For more information, see Find Missing Data.

NOTE: If the data type for the column is URL, then the replacement text string must be a valid URL, or the new data is registered as mismatched with the data type.

Mismatched values preset: This transform converts to 00000 all values in the Zip column that are mismatched against the Zipcode data type. In this case, the preset ISMISMATCHED(Zip, ['Zipcode']) identifies the mismatched values in the column, as compared to the Zipcode data type:

For more information, see Find Bad Data.

Remove a specific sub-string

An entry in the example data contains an additional word: My String extra. You can use a simple replace command to remove it: 

The global parameter causes the replacement to be applied to all instances found within a cell value. Otherwise, the replacement occurs only on the first instance.

Replace double spaces

There are multiple ways of removing double spaces, or any pattern, from text values. For best results, you should limit this change to individual columns.

NOTE: For matching string patterns that are short in length, you should be careful to define the scope of match. For example, to remove double spaces from your dataset, you should limit the columns to just the ones containing string values. If you applied the change to all columns in the dataset, meaningful uses of double spacing could be corrupted, such as in JSON data fields.

Break out CamelCase

CamelCase refers to text in which multiple words are joined together by removing the spaces between them. In the example data, the entry MyString is an example of CamelCase. 

NOTE: Regular expressions are very powerful pattern-matching tools. If they are poorly specified in a transform, they can have unexpected results. Please use them with caution.

You can use  to break up CamelCase entries in a column of values. The following transforms use regular expressions to identify patterns in a set of values: 

The first transform locates all instances of uppercase letters followed by lower-case letters. Each instance is replaced by a space, followed by the found string ($2). For more information, see Text Matching.

Reduce strings by words

Remove last word:

For example, you need to remove the last word of a string and the space before it. You can use the following replace transform to do that:

When the above is previewed, however, you might notice that ending punctuation is not captured. For example, periods, exclamation points, and question marks at the end of your values are not captured in the . To capture those values, the on parameter must be expanded: 

In the second version, a capture group has been inserted in the middle of the on parameter value, as specified by the contents of the parentheses:

Reduce total number of words:

You need to cut each value in a column down to a maximum of two words. You can use the following to identify the first two words using capture groups in a  and then write that pattern back out, dropping the remainder of the column value:

For the on pattern: