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Release 5.1



This example illustrates the different uses of the following transforms to replace or extract cell data:

  • set - defines the values to use in a predefined column. See Set Transform.

    Tip: Use the derive transform to generate a new column containing a defined set of values. See Derive Transform.

  • replace - replaces a string literal or pattern appearing in the values of a column with a specific string. See Replace Transform.
  • extract - extracts a pattern-based value from a column and stores it in a new column. See Extract Transform.


The following dataset contains contact information that has been gathered by your marketing platform from actions taken by visitors on your website. You must clean up this data and prepare it for use in an analytics platform.

LE160301001JonesCharlesChief Technical Officer415-555-1212reg
LE160301002LyonsEdward 415-012-3456download whitepaper
LE160301003MartinMaryCEO510-555-5555delete account
LE160301004SmithTaliaEngineer510-123-4567free trial



Title column: For example, you first notice that some data is missing. Your analytics platform recognizes the string value, "#MISSING#" as an indicator of a missing value. So, you click the missing values bar in the Title column. Then, you select the Replace suggestion card. Note that the default replacement is a null value, so you click Modify and update it:

set col: Title value: IF(ISMISSING([Title]),'#MISSING#',Title)

Request column: In the Request column, you notice that the reg entry should be cleaned up. Add the following transform, which replaces that value:

replace col:Request with:'Registration' on:`{start}reg{end}`

The above transform uses a Alteryx® pattern as the expression of the on: parameter. This expression indicates to match from the start of the cell value, the string literal reg, and then the end of the cell value, which matches on complete cell values of reg only.

This transform works great on the sample, but what happens if the value is Reg with a capital R? That value might not be replaced. To improve the transform, you can modify the transform with the following Alteryx pattern in the on parameter, which captures differences in capitalization:

replace col:Request with:'Registration' on:`{start}{[R|r]}eg{end}`

Add the above transform to your recipe. Then, it occurs to you that all of the values in the Request column should be capitalized in title or proper case:

set col:Request value:PROPER(Request)

Now, all values are capitalized as titles.

Phone column: You might have noticed some issues with the values in the Phone column. In the United States, the prefix 555 is only used for gathering information; these are invalid phone numbers.

In the data grid, you select the first instance of 555 in the column. However, it selects all instances of that pattern, including ones that you don't want to modify. In this case, continue your selection by selecting the similar instance of 555 in the other row. In the suggestion cards, you click the Replace transform.

Notice, however, that the default Replace transform has also highlighted the second 555 pattern in one instance, which could be a problem in other phone numbers not displayed in the sample. You must modify the selection pattern for this transform. In the on: parameter below, the Alteryx pattern has been modified to match only the instances of 555 that appear in the second segment in the phone number format:

replace col: Phone on: `{start}%{3}-555-%*{end}` with: '#INVALID#' global: true

Note the wildcard construct has been added (%*). While it might be possible to add a pattern that matches on the last four characters exactly (%{4}), that matching pattern would not capture the possibility of a phone number having an extension at the end of it. The above expression does.

NOTE: The above transform creates values that are mismatched with the Phone Number data type. In this example, however, these mismatches are understood to be for the benefit of the system consuming your Alteryx output.

LeadId column: You might have noticed that the lead identifier column (LeadId) contains some embedded information: a date value and an identifier for the instance within the day. The following steps can be used to break out this information. The first one creates a separate working column with this information, which allows us to preserve the original, unmodified column:

derive type:single value:LeadId as:'LeadIdworking'

You can now work off of this column to create your new ones. First, you can use the following replace transform to remove the leading two characters, which are not required for the new columns:

replace col:LeadIdworking with:'' on:'LE'

Notice that the date information is now neatly contained in the first characters of the working column. Use the following to extract these values to a new column:

extract col: LeadIdworking on: `{start}%{6}`

The new LeadIdworking2 column now contains only the date information. Cleaning up this column requires reformatting the data, retyping it as a Datetime type, and then applying the dateformat function to format it to your satisfaction. These steps are left as a separate exercise.

For now, let's just rename the column:

rename col:LeadIdworking1 to:'LeadIdDate'

In the first working column, you can now remove the date information using the following:

replace col: LeadIdworking on: `{start}%{6}` with: ''

You can rename this column to indicate it is a daily identifier:

rename col:LeadIdworking to:'LeadIdDaily'



LE160301001001160301JonesCharlesChief Technical Officer#INVALID#Registration
LE160301002002160301LyonsEdward#MISSING#415-012-3456Download Whitepaper
LE160301003003160301MartinMaryCEO#INVALID#Delete Account
LE160301004004160301SmithTaliaEngineer510-123-4567Free Trial



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