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Replace Transform

Note

Transforms are a part of the underlying language, which is not directly accessible to users. This content is maintained for reference purposes only. For more information on the user-accessible equivalent to transforms, see Transformation Reference.

Replaces values within the specified column or columns based on the string literal, pattern, or location within the cell value, as specified in the transform.

The replace transform is used primarily to match on patterns within a string. For entire cell replacement across all rows of the column, use the set transform. See Set Transform.

Basic Usage

on parameter example:

Specifies the string literal or pattern to match.

replace col: text on: 'honda' with:'toyota' global: true

Output: Replaces all instances in the text column of honda with toyota. If honda appears twice a cell, both instances are replaced with toyota.

at parameter example:

Specifies the beginning character and ending character as index values for the match.

replace col: text at: 2,6 with:'replacement text'

Output: For all values in the text column, replace the substring between character 2 and 6 in the column with the value replacement text. If the length of the original cell value is less than 6, the replacement value is inserted.

Syntax and Parameters

replace col:column_ref with:'literal_replacement' [at:(start_index,end_index)] [on:string_literal_pattern] [global:true|false]

Token

Required?

Data Type

Description

replace

Y

transform

Name of the transform

col

Y

string

Name of column where to make replacements

with

Y

see below

Literal value with which to replace matched values

after

N

string

String literal or pattern that precedes the pattern to match

at

N

Array

Two-integer array identifying the character indexes of start and end characters to match

before

N

string

String literal or pattern that appears after the pattern to match

from

N

string

String literal or pattern that identifies the start of the pattern to match

to

N

string

String literal or pattern that identifies the end of the pattern to match

on

N

string

String literal or pattern that identifies the cell characters to replace

global

N

boolean

If true, all occurrences of matches are replaced. Default is false.

For more information on syntax standards, see Language Documentation Syntax Notes.

col

Identifies the column or columns to which to apply the transform. You can specify one or more columns.

To specify multiple columns:

  • Discrete column names are comma-separated.

  • Values for column names are case-sensitive.

replace col: MyCol on: 'MyString' with: 'myNewString'

Output: Replaces value MyString in MyCol column with myNewString.

Usage Notes:

Required?

Data Type

Yes

String (column name)

with

Merge Columns transformation: Specifies the delimiter between columns that are merged. If this parameter is not specified, no delimiter is applied.

Replace Text or Pattern transformation: Specifies the replacement value.

For the replace transform, this value must be a literal value. You can apply values of String or other data types. After replacement, the column data type is re-inferred.

Note

Some regular expression capture groups with references (such as $2) are supported across all running environments. See Capture Group References.

Usage Notes:

Required?

Data Type

Yes

Literal of any data type

after

replace col:Primary_URL with:'' after:`http({any}|):`

Output: All content after the protocol identifier (http: or https:) is dropped.

A pattern identifier that precedes the value or pattern to match. Define the after parameter value using string literals, regular expressions, or Wrangle .

Usage Notes:

Required?

Data Type

No

String (string literal or pattern)

  • The after and from parameters are very similar. from includes the matching value as part of the replaced string.

  • after can be used with either to, on, or before. See Pattern Clause Position Matching.

at

replace col: MyCol at: 2,6 with:'MyNewString'

Output: Replace contents of MyCol that starts at the second character in the column and extends to the sixth character with the value MyNewString.

Identifies the start and end point of the pattern to interest.

Parameter inputs are in the form of x,y where x and y are positive integers indicating the starting character and ending character, respectively, of the pattern of interest.

  • x must be less than y.

  • If y is greater than the length of the value, the pattern is defined to the end of the value, and a match is made.

Usage Notes:

Required?

Data Type

Must use eitheronoratparameter

Array of two Integers (X,Y)

before

A pattern identifier that occurs after the value or pattern to match. Define the pattern using string literals, regular expressions, or Wrangle .

replace col:credit_card with:'****-***-***-' after:`{start}` before:`({digit}{4}){end}`

Output:

  • Replaces first three groups of digits in the credit_card column with asterisks, effectively masking the number.

Usage Notes:

Required?

Data Type

No

String or pattern

  • The before and to parameters are very similar. to includes the matching value as part of the replaced string.

  • before can be used with either from, on, or after. See Pattern Clause Position Matching.

from

Identifies the pattern that marks the beginning of the value to match. It can be a string literal, Wrangle , or regular expression. The from value is included in the match.

replace col: MyCol from: '<END>' with: ''

Output:

  • All content from the string <END> to the end of the string value in MyCol is removed.

Usage Notes:

Required?

Data Type

No

String or pattern

  • The after and from parameters are very similar. from includes the matching value as part of the replaced string.

  • from can be used with either to or before. See Pattern Clause Position Matching.

to

Identifies the pattern that marks the ending of the value to match. Pattern can be a string literal, Wrangle , or regular expression. The to value is included in the match.

replace col:ssn with:'***-**-' to:`{digit}{3}-{digit}{2}-`

Output:

  • Replace first two number groups in the column ssn with asterisks to mask the data.

Usage Notes:

Required?

Data Type

No

String or pattern

  • The before and to parameters are very similar. to includes the matching value as part of the replaced string.

  • to can be used with either from or after. See Pattern Clause Position Matching.

on

replace col: MyCol on: `###ERROR` with:'No error here'

Identifies the pattern to match, which can be a string literal, Wrangle , or regular expression.

Astuce

You can insert the Unicode equivalent character for this parameter value using a regular expression of the form /\uHHHH/. For example, /\u0013/ represents Unicode character 0013 (carriage return). For more information, see Supported Special Regular Expression Characters.

Usage Notes:

Required?

Data Type

Must use eitheronoratparameter

String or pattern

global

Indicates whether any match should be applied to one instance or to all.

  • (Default) If false, apply transform only to the first instance.

  • If true, apply to all found matches.

Note

If you have specified the pattern to match with the at parameter, then the number of possible replacement instances is only 1, so the global parameter is not used.

Usage Notes:

Required?

Data Type

No. Default is false.

Boolean

Examples

Astuce

For additional examples, see Common Tasks.

Example - Clean up marketing contact data with replace, set, and extract

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.

Note

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

Example - Using capture group references for replacements

The replace transform can take advantage of capture groups defined in the Wrangle and regular expressions used to search for values within a column. A capture group is a sub-pattern within your pattern that defines a value that you can reference in the replacement.

Note

For this transform, capture groups can be specified in the on parameter only.

In the following example, the on parameter defines two capture groups, and the with parameter references them in the replacement. In this example, any entry in the camel_case column that contains a lower-case letter followed immediately by an upper-case letter is replaced by the same value with a space inserted in the middle. The $1 value references the first capture group in the corresponding Wrangle :

Transformation Name

Replace text or pattern

Parameter: Column

camel_case

Parameter: Find

`({lower})({upper})`

Parameter: Replace with

'$1 $2'

Parameter: Match all occurrences

true

Capture Group

Description

Replacement Reference

({lower})

A single lower-case letter

$1
({upper})

A single upper-case letter

$2