is the domainspecific language used to build transformation recipes in
.
A
recipe is a sequence of transformation steps applied to your dataset in order to produce your results.
Transform and transformation:
For most of these actions, you can pass one or more parameters to define the context (columns, rows, or conditions).
Function:
 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 transformation that you can add to your recipe.
Tip 

Tip: Where possible, you should make selections in the data grid to build transformation steps. These selections prompt a series of cards to be displayed. You can select different cards to specify a basic transformation for your selected data, choose a variant of that transformation, and then modify the underlying recipe as necessary. For more information, see Overview of Predictive Transformation.For more information on the suggestion cards, see Selection Details Panel. Some complex transformations, such as joins and unions, must be created through dedicated screens. See Transformer Page. 
vs. SQL
Info 

NOTE: is not SQL. It is a proprietary language of data transformation, purposebuilt for . 
While there are some overlaps between
and SQL, here are the key distinctions:
 is a proprietary language designed for data transformation. Every supported transformation is designed to make changes to a dataset. It cannot be used to read from or write to a datastore.
 Users interact with exclusively through the . There is no direct interaction with the language.
 SQL (Structured Query Language) is designed for querying, transforming, and writing for relational datasources. It cannot be applied to filebased datasets.
 SQL cannot be used to transform data in .
Syntax
transforms follow this general syntax:
D code 

(transform) param1:(expression) param2:(expression) 
Transform Element  Description 

transform  In , a transform (or verb) is a single keyword that identifies the type of change you are applying to your dataset. A transform is always the first keyword in a recipe step. Details are below. 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. Info 

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 value parameter defines the expression that creates the output value or values stored when the transform is executed. An expression can contain combinations of the following:  Functions apply computations or evaluations of source data, the outputs of which can become inputs to the column. Sources may be constants or column references. A function reference is always followed by brackets (), even if it takes no parameters. See below.
 Operators are singlecharacter representations of numeric functions, comparisons, or logical operators. For example, the plus sign (
+ ) is the operator for the add function. See below.  Constants can be quoted string literals (
'mystring' ), Integer values (1001 ), Decimal values (1001.01 ), Boolean values (true or false ) or patterns. For more information on , see Text Matching.

col:  When present, the col parameter identifies the name of the column or columns to which the transform is applied. Some transforms may support multiple columns as a list, as a range of columns (e.g., column1~column5 ), or all columns in the dataset (using wildcard indicator, col: * ). 
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 window , pivot , and derive , the group parameter enables you to calculate aggregation functions within a group value. For example, you can sum sales for each rep by applying group:repName to your transformation. 
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 . 
The following types of parameter inputs may be referenced in a transform's parameters.
Other
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.
D trans 

RawWrangle  true 

p03Value  'myNewCol' 

Type  step 

WrangleText  derive type:single value: myCol as:'myNewCol' 

p01Name  Formula type 

p01Value  Single row formula 

p02Name  Formula 

p02Value  myCol 

p03Name  New column name 

SearchTerm  New formula 


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:
D trans 

RawWrangle  true 

p03Value  src Column 

Type  step 

WrangleText  rename type: manual mapping: [srcColumn, 'src Column'] 

p01Name  Option 

p01Value  Manual rename 

p02Name  Column 

p02Value  srcColumn 

p03Name  New column name 

SearchTerm  Rename columns 


Info 

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
.
Example:
D trans 

RawWrangle  true 

p03Value  'six' 

Type  step 

WrangleText  derive type:single value:MULTIPLY(3,2) as:'six' 

p01Name  Formula type 

p01Value  Single row formula 

p02Name  Formula 

p02Value  MULTIPLY(3,2) 

p03Name  New column name 

SearchTerm  New formula 


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.
D trans 

RawWrangle  true 

Type  step 

WrangleText  set col: val1,val2 value: ABS($col) 

p01Name  Columns 

p01Value  val1,val2 

p02Name  Formula 

p02Value  ABS($col) 

SearchTerm  Edit column with formula 


$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):
D trans 

RawWrangle  true 

p03Value  'three' 

Type  step 

WrangleText  derive type:single value:ROUND(DIVIDE(10,3),0) as:'three' 

p01Name  Formula type 

p01Value  Single row formula 

p02Name  Formula 

p02Value  ROUND(DIVIDE(10,3),0) 

p03Name  New column name 

SearchTerm  New formula 


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
:
D trans 

RawWrangle  true 

p03Value  'my13' 

Type  step 

WrangleText  derive type:single value: (5 + 8) as:'my13' 

p01Name  Formula type 

p01Value  Single row formula 

p02Name  Formula 

p02Value  (5 + 8) 

p03Name  New column name 

SearchTerm  New formula 


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:
D trans 

RawWrangle  true 

p03Value  'circumference' 

Type  step 

WrangleText  derive type:single value: (3.14159 * diameter) as: 'circumference' 

p01Name  Formula type 

p01Value  Single row formula 

p02Name  Formula 

p02Value  (3.14159 * diameter) 

p03Name  New column name 

SearchTerm  New formula 


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
.
D trans 

RawWrangle  true 

p03Value  'bigOrder' 

Type  step 

WrangleText  derive type:single value:IF(order > 1000000, true, false) as:'bigOrder' 

p01Name  Formula type 

p01Value  Single row formula 

p02Name  Formula 

p02Value  IF(order > 1000000, true, false) 

p03Name  New column name 

SearchTerm  New formula 


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
.
D trans 

RawWrangle  true 

p03Value  'StringCol' 

Type  step 

WrangleText  derive type:single value:'myString' as:'StringCol' 

p01Name  Formula type 

p01Value  Single row formula 

p02Name  Formula 

p02Value  'myString' 

p03Name  New column name 

SearchTerm  New formula 


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
):
D trans 

RawWrangle  true 

p03Value  `%{3}%{2}%{4}` 

Type  step 

WrangleText  extract col: MyData on:`%{3}%{2}%{4}` limit:10 

p01Name  Column to extract from 

p01Value  MyData 

p02Name  Option 

p02Value  Custom text or pattern 

p03Name  Text to extract 

p04Value  10 

p04Name  Number of matches to extract 

SearchTerm  Extract text or pattern 


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:
D trans 

RawWrangle  true 

p03Value  '' 

Type  step 

WrangleText  replace col: qty on: /^\d$^\d\d$/ with: '' global: true 

p01Name  Column 

p01Value  qty 

p02Name  Find 

p02Value  /^\d$^\d\d$/ 

p03Name  Replace with 

p04Value  true 

p04Name  Match all occurrences 

SearchTerm  Replace text or pattern 


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:
D trans 

RawWrangle  true 

Type  step 

WrangleText  derive type:single value:DATEFORMAT(myDate, 'yyyymmdd') 

p01Name  Formula type 

p01Value  Single row formula 

p02Name  Formula 

p02Value  DATEFORMAT(myDate, 'yyyymmdd') 

SearchTerm  New formula 


Array
A valid array of values matching the Array data type.
Example:
Code Block 

[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
):
D trans 

RawWrangle  true 

Type  step 

WrangleText  derive type:single value: ARRAYLEN('["red", "orange", "yellow", "green", "blue", "indigo", "violet"]') 

p01Name  Formula type 

p01Value  Single row formula 

p02Name  Formula 

p02Value  ARRAYLEN('["red", "orange", "yellow", "green", "blue", "indigo", "violet"]') 

SearchTerm  New formula 


Object
A valid set of values matching the Object data type.
Example:
Code Block 

{"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:
D trans 

RawWrangle  true 

Type  step 

WrangleText  unnest col:myCol keys:'brand','model','color' 

p01Name  Column 

p01Value  myCol 

p02Name  Paths to elements 

p02Value  'brand','model','color' 

SearchTerm  Unnest Objects into columns 


Interactions between
and the Application
 As you build 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 transformation to the sample in the data grid.
Info 

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 transformation is invalid, the data grid is grayed out.
 For more information, see Transform Preview.
 When you add the transformation 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 transformation step in your recipe whenever needed.
 When you edit a transformation 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 transformation steps in the data grid. Your changes may cause some recipe steps to become invalid.
 See Recipe Panel.
A transformation is an action for which you can browse or search through the Transform Builder in the Transformer page. When specified and added to the recipe, these sometimes complex actions are rendered in the recipe as steps using the underlying transforms of the language.
Tip 

Tip: Through transformations, guides you through creation of more sophisticated steps that would be difficult to create in raw . 
For more information on the list of available transformations, see Transformation Reference.
For more information on creating transformation steps in the Transformer page, see Transform Builder.
Functions
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.
Below, function inputs are listed in increasing order of generality.
Info 

NOTE: A function cannot take a higherorder parameter input type without taking the lower parameter input types. For example, a function cannot take a nested function as an input if it does not accept a literal value, too. 
Order  Parameter input type  Example 

1  literal  Code Block 

FUNCTION('my input') 

2  column  Code Block 

FUNCTION(myColumnOfValues) 

3  function  Code Block 

FUNCTION(SUM(MyCol)) 

Function categories
Function Category  Description 

Aggregate Functions  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.
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.
Transforms are described in the Language Appendices. For more information, see Transforms.
Documentation
Documentation for
is also available through
. Select
Help menu > Documentation.
Tip 

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