Contents:
Wrangle is the domainspecific language used to build transformation recipes in Designer Cloud Enterprise Edition.
A Wrangle recipe is a sequence of transforms applied to your dataset in order to produce your results.
 A transform is a single action applied to a dataset. For most transforms, you can pass one or more parameters to define the context (columns, rows, or conditions).
 Some parameters accept one or more functions. A function is a computational action performed on one or more columns of data in your dataset.
 Recipes are built in the Transformer Page. See Transformer Page.
When you select suggestions in the Transformer Page, your selection is converted into a Wrangle command and added to your recipe.
Tip: Where possible, you should make selections in the data grid to build transform steps. These selections prompt a series of cards to be displayed. You can select different cards to specify a basic transform for your selected data, choose a variant of that transform, and then modify the underlying Wrangle recipe as necessary. For more information, see Overview of Predictive Transformation.
For more information on the suggestion cards, see Selection Details Panel.
Some complex transforms, such as joins and unions, must be created through dedicated screens. See Transformer Page.
Wrangle Syntax
Wrangle transforms follow this general syntax:
(transform) param1:(expression) param2:(expression)
Transform Element  Description 

transform  A transform (or verb) is a single keyword that identifies the type of change you are applying to your dataset.
The other elements in each step are contextual parameters for the transform. Some transforms do not require parameters. 
parameter1: , parameter2:  Additional parameters may be optional or required for any transform. NOTE: A parameter is always followed by a colon. A parameter may appear only one time in a transform step. 
Common Parameters
Depending on the transform, one or more of value
, col
, and row
parameters may be used. For example, the set
transform can use all three or just value
and col
.
Transform Element  Description 

value:  When present, the An expression can contain combinations of the following:

col:  When present, the Some transforms may support multiple columns as a list, as a range of columns (e.g., 
row:  When present, the row parameter defines the expression to evaluate to determine the rows on which to perform the transform. If the row expression evaluates to true for a row, the transform is performed on the row. 
group:  For aggregating transforms, such as NOTE: Transforms that use the group parameter can result in nondeterministic reordering in the data grid. However, you should apply the group parameter, particularly on larger datasets, or your job may run out of memory and fail.
To enforce row ordering, you can use the sort transform. For more information, see Sort Transform.

order:  For aggregating transforms, such as window , pivot , and derive , the order parameter can be used to specify the column by which the transform results are sorted. In the previous example, you might choose to sort your sum of sales calculation by state: order:State . 
Parameter Inputs
The following types of parameter inputs may be referenced in a transform's parameters.
Other Alteryx data types can be referenced as column references. For literal values of these data types, you can insert them into your expressions as strings. Transforms cause the resulting values to be reinferred for their data type.
Column reference
A reference to the values stored in a column in your dataset. Columns can be referenced by the plaintext value for the column name.
Example: value
parameter references the myCol
column.
Transformation Name  New formula 

Parameter: Formula type  Single row formula 
Parameter: Formula  myCol 
Parameter: New column name  'myNewCol' 
Column names with spaces or special characters in a transformation must be wrapped by curly braces.
Example: Below, srcColumn
is renamed to src Column
, which requires no braces because the new name is captured as a string literal:
Transformation Name  Rename columns 

Parameter: Option  Manual rename 
Parameter: Column  srcColumn 
Parameter: New column name  src Column 
NOTE: Current column names that have a space in them must be bracketed in curly braces. The above column name reference is the following: {src Column}
.
Functions
Some parameters accept functions as inputs. Where values or formulas are calculated, you can reference one of the dozens of functions available in Wrangle.
Example:
Transformation Name  New formula 

Parameter: Formula type  Single row formula 
Parameter: Formula  MULTIPLY(3,2) 
Parameter: New column name  'six' 
Metadata variables
Wrangle supports the use of variable references to aspects of the source data or dataset. In the following example, the ABS function is applied to each column in a set of them using the $col
reference.
Transformation Name  Edit column with formula 

Parameter: Columns  val1,val2 
Parameter: Formula  ABS($col) 
$col
returns the value of the current row. For more information on these variables, see Source Metadata References.
Nested expressions
Individual parameters within a function can be computed expressions themselves. These nested expressions can be calculated using constants, other functions, and column references.
Example: Computes a column whose only value is ten divided by three, rounded to the nearest integer (3):
Transformation Name  New formula 

Parameter: Formula type  Single row formula 
Parameter: Formula  ROUND(DIVIDE(10,3),0) 
Parameter: New column name  'three' 
Integer
A valid integer value within the accepted range of values for the Integer datatype. For more information, see Supported Data Types.
Example: Generates a column called, my13
which is the sum of the Integer values 5
and 8
:
Transformation Name  New formula 

Parameter: Formula type  Single row formula 
Parameter: Formula  (5 + 8) 
Parameter: New column name  'my13' 
Decimal
A valid floating point value within the accepted range of values for the Decimal datatype. For more information, see Supported Data Types.
Example: Generates a column of values that computes the approximate circumference of the values in the diameter
column:
Transformation Name  New formula 

Parameter: Formula type  Single row formula 
Parameter: Formula  (3.14159 * diameter) 
Parameter: New column name  'circumference' 
Boolean
A true
or false
value.
Example: If the value in the order
column is more than 1,000,000, then the value in the bigOrder
column is true
.
Transformation Name  New formula 

Parameter: Formula type  Single row formula 
Parameter: Formula  IF(order > 1000000, true, false) 
Parameter: New column name  'bigOrder' 
String
A string literal value is the baseline datatype. String literals must be enclosed in single quotes.
Example: Creates a column called, StringCol
containing the value myString
.
Transformation Name  New formula 

Parameter: Formula type  Single row formula 
Parameter: Formula  'myString' 
Parameter: New column name  'StringCol' 
Alteryx pattern
Designer Cloud Enterprise Edition supports a special syntax, which simplifies the generation of matching patterns for string values.
Patterns must be enclosed in accent marks ( `MyPattern`
). For more information, see Text Matching.
Example: Extracts up to 10 values from the MyData
column that match the basic pattern for social security numbers (XXXXXXXXX
):
Transformation Name  Extract text or pattern 

Parameter: Column to extract from  MyData 
Parameter: Option  Custom text or pattern 
Parameter: Text to extract  `%{3}%{2}%{4}` 
Parameter: Number of matches to extract  10 
Regular expression
Regular expressions are a common standard for defining matching patterns. Regex is a very powerful tool but can be easily misconfigured.
Regular expressions must be enclosed in slashes ( /MyPattern/
).
Example: Deletes all twodigit numbers from the qty
column:
Transformation Name  Replace text or pattern 

Parameter: Column  qty 
Parameter: Find  /^\d$^\d\d$/ 
Parameter: Replace with  '' 
Parameter: Match all occurrences  true 
Datetime
A valid date or time value that matches the requirements of the Datetime datatype. See Supported Data Types.
Datetime values can be formatted with specific formatting strings. See DATEFORMAT Function.
Example: Generates a new column containing the values from the myDate
column reformatted in yyyymmdd
format:
Transformation Name  New formula 

Parameter: Formula type  Single row formula 
Parameter: Formula  DATEFORMAT(myDate, 'yyyymmdd') 
Array
A valid array of values matching the Array data type.
Example:
[0,1,2,3,4,5,6,7,8]
See Supported Data Types.
Example: Generates a column with the number of elements in the listed array (7
):
Transformation Name  New formula 

Parameter: Formula type  Single row formula 
Parameter: Formula  ARRAYLEN('["red", "orange", "yellow", "green", "blue", "indigo", "violet"]') 
Object
A valid set of values matching the Object data type.
Example:
{"brand":"Subaru","model":"Impreza","color","green"}
See Supported Data Types.
Example: Generates separate columns for each of the specified keys in the object ( brand
, model
, color
), containing the corresponding value for each row:
Transformation Name  Unnest Objects into columns 

Parameter: Column  myCol 
Parameter: Paths to elements  'brand','model','color' 
Interactions between Wrangle and the Application
 As you build Wrangle steps in the Transform Builder, your syntax is validated for you. You cannot add steps containing invalid syntax.
 Error messages are reported back to the application, so you can make immediate modifications to correct the issue.
 Typeahead support can provide guidance to the supported transforms, functions, and column references.
 For more information, see Transform Builder.
 When you have entered a valid transform step, the results are previewed for you in the data grid.
This preview is generated by applying the transform to the sample in the data grid.
NOTE: The generated output applies only to the values displayed in the data grid. The function is applied across the entire dataset only during job execution.
 If the previewed transform is invalid, the data grid is grayed out.
 For more information, see Transform Preview.
 When you add the transform to your recipe:
 It is applied to the sample in the application, and the data grid is updated to the current state.
 Column histograms are updated with new values and counts.
 Column data types may be reinferred for affected columns.
 Making changes:
 You can edit any transform step in your recipe whenever needed.
 When you edit a transform step in your recipe, the context of the data grid is changed to display the state of your data up to the point of previewing the step you're editing.
 All subsequent steps are still part of the recipe, but they are not applied to the sample yet.
 You can insert recipe steps between existing steps.
 When you delete a recipe step, the state remains at the point where the step was removed.
 You can insert a new step if needed.
 When you complete your edit, select the final step of the recipe, which displays the results of all of your transform steps in the data grid. Your changes may cause some recipe steps to become invalid.
 See Recipe Panel.
 You can edit any transform step in your recipe whenever needed.
Transforms
A transform, or verb, is an action applied to rows or columns of your data. Transforms are the essential set of changes that you can apply to your dataset.
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 useraccessible equivalent to transforms, see Transformation Reference.
For more information, see Transforms.
Function Categories
A function is an action that is applied to a set of values as part of a transform step. Functions can apply to the values in a transform for specific data types, such as strings, or to types of transforms, such as aggregate and window function categories. A function cannot be applied to data without a transform.
Function Category  Description 

These functions are used to perform aggregation calculations on your data, such as sum, mean, and standard deviation.  
Comparison Functions  Comparison functions enable evaluation between two data elements, which are typically nested (Object or Array) elements. 
Math Functions  Perform computations on your data using a variety of math functions and numeric operators. 
Trigonometry Functions  Calculate standard trigonometry functions as well as arc versions of them. 
Date Functions  Use these functions to extract data from or perform operations on objects of Datetime data type. 
String Functions  Manipulate strings, including finding substrings within a string. 
Nested Functions  These functions are designed specifically to assist in wrangling nested data, such as Objects, Arrays, or JSON elements. 
Type Functions  Use the Type functions to identify valid, missing, mismatched, and null values. 
Window Functions  The Window functions enable you to perform calculations on relative windows of data within your dataset. 
Other Functions  Miscellaneous functions that do not fit into the other categories 
Operator Categories
An operator is a single character that represents an arithmetic function. For example, the Plus sign (+
) represents the add function.
Operator Category  Description 

Logical Operators  and, or, and not operators 
Numeric Operators  Add, subtract, multiply, and divide 
Comparison Operators  Compare two values with greater than, equals, not equals, and less than operators 
Ternary Operators  Use ternary operators to create if/then/else logic in your transforms. 
Documentation
Documentation for Wrangle is also available through Designer Cloud Enterprise Edition. Select Help menu > Documentation.
Tip: When searching for examples of functions, try using the following form for your search terms within the Product Documentation site:
 Functions:
wrangle_function_NameOfFunction
All Topics
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