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The Transform Builder enables you to

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rapidly assemble complete transform steps through a simple menu-driven interface.

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After you select the transformation to apply, all relevant parameters can be configured through selection or type-ahead fields, so that you can choose from only the elements that are appropriate for the selected transformation.

To open the Transform Builder, begin creating a step through one of the following methods:

• Select a transformation from the Transformer toolbar. See Transformer Toolbar.
• Click the Macros icon in the toolbar to apply a macro as your next step. See Apply a Macro.
• Select a transformation from a column menu. See Column Menus.
• Search for and select a transformation in the Search panel. See Search Panel.
• Click New Step in in the Recipe panel. See Recipe Panel.
• Edit an existing step.

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Transform Builder

Keyboard shortcuts:

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Step 1 - Select transformation in the Search Panel

From the Search panel, begin typing to see the list of available transformations. Select your preferred one.

Join and join and union transformations  transforms have dedicated pages for configuring this transformations. You can enter join datasets or  or union as as the search term transformation to open the corresponding tool:.

• See Join Window.
• See Union Page.

For a list of available transformations, see see Transformation Reference.

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Depending on the transform that you have selected, you must specify one or more of the following types of parameters in the Transform Builder.

• Some transforms support combinations of the following. 
• Some transforms, like like deduplicate, require no parameters. 

The following are general categories of object typesparameters:

• Literal values. A literal, or constant, value is a fixed numeric, string, Boolean, or other type of value, which does not change depending on the row under evaluation.
• Functions. 

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   supports a wide variety of numerical, statistical, and other function types. For  For a list of available transforms and functions, see Wrangle Language Index.

• Columns. When a column name is used in a formula, the transform uses the value in the named column for the currently evaluated row. 
• Operators. You can apply logical, numeric, or comparison operators as part of your formula.
  
  • See Logical Operators.
  • See Numeric Operators.
  • See Comparison Operators.
• Parameters: Add a reference to a flow parameter in your transformation. See Manage Parameters Dialog.
  
• Metadata.  You You can insert special strings that evaluate to references of your dataset's metadata. For more information, see Source Metadata References.

Columns

Using the Columns parameter, you can select Select or specify the column or columns to which to apply the transform.

The following options are available when specifying one or more columns in a transformation:

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Advanced: Specify the columns using a comma-separated list. You can combine multiple and range options under Advanced. Ranges of columns can be specified using the tilde (~) character. Example:

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Patterns

For some transforms, you can specify patterns to identify conditions or elements of the data on which to take action. These matching patterns can be specified using one of the following types. 

For more information on pattern-based matching, see Text Matching

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Flow parameter: You can also insert a flow parameter into your pattern-based inputs in the Transform Builder. To reference a flow parameter, click the Parameterize icon above any field that accepts pattern-based inputs.

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Delimiter Groups

In the Transform Builder, transforms that require delimiter are organized into delimiter groups, so that you specify only the elements of a pattern that work together. Delimiter groups apply to the following transforms:

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For more information on the underlying syntax for delimiter groups, see Pattern Clause Position Matching.

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Condition

A A condition is is an expression that yields a true or  or false value value. A condition may include all of the elements of a formula. This value determines whether the transformation is applied to the evaluated row.

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Group parameter: For transforms that aggregate data, such as pivot or window, you can specify the column by which you wish to group the computed aggregations. In the following example, all values in the POS_Sales column column are summed up for each value in the Store_Nbr column column.

Assuming that there are entries in the the Store_Nbr column column, the resulting transform step has 50 rows, each of which contains the total sales for the listed store number.

Order parameter: Some transforms support the order parameter parameter, which allows you to specify the column of values that are used to sort the output. In the following example, all aggregates Sales values values are ordered by the contract date and grouped by State:

The output can always be ordered using the sort transform. See Sort Transform.

New Column Name parameter: For transforms that generate new columns, such as derive and  and extract, you can optionally specify the name of the new column, which saves adding a step to rename it. In the following example, the values of colA and  and colB are are summed and written to the new column colC:

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When you have finished your transform step, review the preview in the data grid.

If the results look ok, click  click  Add.

The step is added to your recipe and applied to the data grid.

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After you have added a step, you can modify it as needed. In the Recipe panel, select the Pencil icon next to the recipe step. The step is displayed for editing in the Transform Builder. D s alsoinCQLtruelabel(label = "transformation_ui") OR (label = "transform_ui")