Data Pivot

Data Pivot

Data Pivot is located under Model Studio (  ) in Data Preparation, in the left task pane. Use the drag-and-drop method to use the algorithm on the canvas. Click the algorithm to view and select different properties for analysis.

Refer to Properties of Data Pivot.


Properties of Data Pivot

The available properties of Data Pivot are shown in the figure below.


The depictions of tables and the change from one table structure to another are shown in the figure above. The elements A, B, and C of the first table are rearranged as shown in the second table.
The table below describes different fields present on the properties of Data Pivot.

Field

Description

Remark

Run

It allows you to run the node.-
ExploreIt allows you to explore the successfully executed node.-
Vertical Ellipses

The available options are

  • Run till node
  • Run from node
  • Publish as a model
  • Publish code
-

Task Name


It is the name of the task selected on the workbook canvas.

  • You can click the text field to edit or modify the task's name as required.
  • While editing the task name, make sure there is no space between words, dots, or special characters used.

Index Feature (A)


It allows you to select the variable to be selected for the first column of the resultant table.

  • You can select any number of variables.
  • You can select a categorical or a numerical variable.

Feature to Pivot (B)


It allows you to select the variable(s) to be selected for the remaining columns of the resultant table.

  • These are the variables that are pivoted.
  • You can select a categorical or a numerical variable
  • You can select only one variable.
  • The variable selected as Index Feature (A) cannot be selected here. If you select it, the variable gets unselected and disappears from the Index Feature (A) selection.

Unique values of features to Pivot

It allows you to select unique variable values as column names in the resultant table.

  • These columns are related to the variables selected in the Feature to Pivot (B) option.
  • Click Add to open the Output Columns window. In the window, you can,
  • Add a new variable as a unique value.
  • Select unique values of the variable selected in the Feature to Pivot (B) option. For this, click Add all unique values. All unique values of the variable appear listed in the window.
  • You can delete any unwanted unique value from the list by clicking the corresponding delete icon.

Feature to Aggregate (C)


It allows you to select the variable to be aggregated based on the variable selected as Feature to Pivot (B).

  • You can select only a numerical variable.
  • You can select multiple variables.
  • For this variable, you can Select both Measures and Dimensions.
  • The unique values in Output Columns names are attached with these feature names to create column headings in the resultant table.
  • Based on the variable type, you can select any one of the following methods for aggregation.
  • Numerical Variable:
    • Average (default)
    • Count
    • Count (distinct)
    • Maximum
    • Minimum
    • Sum
  • Categorical Variable:
    • Count
    • Count (distinct)
    • Maximum
    • Minimum
  • Interval Variable:
    • Count
    • Count (distinct)
  • To select the aggregation method, click the gear icon next to the variable name. Select the appropriate method from the Select Function pop-up.

Advanced

Node Configuration

It allows you to select the instance of the Amazon Web Services (AWS) server to provide control on the execution of a task in a workbook or workflow.

For more details, refer to Worker Node Configuration.

Example of Data Pivot

Consider a NewSalesData dataset containing information on the zone-wise sale of items by various sales representatives along with the unit cost and total. A snippet of the input data is shown in the figure below.



We select the following properties and apply Data Pivot.

Index Feature (A)

Item

Feature to Pivot (B)

Zone

Output Columns (unique values)

Central, East, and West

Feature to Aggregate (C)

Total (Average)


The figure below shows the resultant Pivoted table on the Data tab.
You can see that

  • The table structure has changed from simple to cross, that is from a tall table to a wide one.
  • The Total values are aggregated in second, third, and fourth columns by mean, for Central and East zones, respectively.



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