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EXAMPLE - Replacement Transforms

This example illustrates the different uses of the replacement transformations to replace or extract cell data.

Source:

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.

LeadId

LastName

FirstName

Title

Phone

Request

LE160301001

Jones

Charles

Chief Technical Officer

415-555-1212

reg

LE160301002

Lyons

Edward

415-012-3456

download whitepaper

LE160301003

Martin

Mary

CEO

510-555-5555

delete account

LE160301004

Smith

Talia

Engineer

510-123-4567

free trial

Transformation:

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 Edit and update it:

Transformation Name

Edit column with formula

Parameter: Columns

Title

Parameter: Formula

if(ismissing([Title]),'#MISSING#',Title)

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

Transformation Name

Replace text or pattern

Parameter: Column

Request

Parameter: Find

`{start}reg{end}`

Parameter: Replace with

Registration

The above transformation uses a Wrangle 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 transformation 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 transformation, you can modify the transformation with the following Wrangle in the on parameter, which captures differences in capitalization:

Transformation Name

Replace text or pattern

Parameter: Column

Request

Parameter: Find

`{start}{[R|r]}eg{end}`

Parameter: Replace with

'Registration'

Add the above transformation 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:

Transformation Name

Edit column with formula

Parameter: Columns

Request

Parameter: Formula

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 Text or Pattern transformation.

Notice, however, that the default Replace Text or Pattern transformation 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 transformation. In the on: parameter below, the Wrangle has been modified to match only the instances of 555 that appear in the second segment in the phone number format:

Transformation Name

Replace text or pattern

Parameter: Column

Phone

Parameter: Find

`{start}%{3}-555-%*{end}`

Parameter: Replace with

'#INVALID#'

Parameter: Match all occurrences

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.

Anmerkung

The above transformation 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:

Transformation Name

New formula

Parameter: Formula type

Single row formula

Parameter: Formula

LeadId

Parameter: New column name

'LeadIdworking'

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

Transformation Name

Replace text or pattern

Parameter: Column

LeadIdworking

Parameter: Find

'LE'

Parameter: Replace with

''

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:

Transformation Name

Extract text or pattern

Parameter: Column to extract from

LeadIdworking

Parameter: Option

Custom text or pattern

Parameter: Text to extract

`{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:

Transformation Name

Rename columns

Parameter: Option

Manual rename

Parameter: Column

LeadIdworking1

Parameter: New column name

'LeadIdDate'

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

Transformation Name

Replace text or pattern

Parameter: Column

LeadIdworking

Parameter: Find

`{start}%{6}`

Parameter: Replace with

''

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

Transformation Name

Rename columns

Parameter: Option

Manual rename

Parameter: Column

LeadIdworking

Parameter: New column name

'LeadIdDaily'

Results:

LeadId

LeadIdDaily

LeadIdDate

LastName

FirstName

Title

Phone

Request

LE160301001

001

160301

Jones

Charles

Chief Technical Officer

#INVALID#

Registration

LE160301002

002

160301

Lyons

Edward

#MISSING#

415-012-3456

Download Whitepaper

LE160301003

003

160301

Martin

Mary

CEO

#INVALID#

Delete Account

LE160301004

004

160301

Smith

Talia

Engineer

510-123-4567

Free Trial