In the Transformer page:

• You can create new columns, modify them, and delete them to re-scope the size of your data to the most meaningful information. 
• You can reshape your data through pivots and aggregations. 
• Nested data in the form of Arrays or Objects (key-value pairs) can be un-nested across columns and rows for easier manipulation. As needed, patterned data can be re-nested through transformations that are easy to select and manipulate.

Build Pivot Tables

You can reshape your data by building pivot tables. Pivot tables are useful when you want to calculate aggregation functions, such as sums, maximums, and averages for one or more columns of data.

In the following example, the data is reshaped to include the sum of POS_Sales for each distinct value in the Daily column across the values in the Sales_Description column:

Reshape your data using pivot tables 

Create Aggregations

An aggregation is a computation across a grouped set of rows. 

• To an entire column (called a flat aggregation)
• To generate a new column
• To use to reshape your entire table

For more information, see Create Aggregation Calculations.

Nest and Unnest 

You can combine data in separate columns into single-column values stored in Arrays or Objects (maps). Similarly, data from an Array or Object column can be converted into new rows or columns based on the keys in the source data. 

For more information, see Nested Data Basics.

Select Columns

You can select a set of columns to completely replace the current dataset. See Select Columns.

Delete Columns

You can reshape your data by deleting unwanted columns in the dataset. You can delete a single column or multiple columns. 

• To delete a column from your dataset, click the required column and select Delete from the column drop-down. 
• If you select Delete others, all other remaining columns are deleted except the selected column.

Reshape your data using Delete columns

The above menu choices get turned into recipe steps that use the Delete columns transformation.  

Split Columns

You can split a column based on one or more known delimiters or based on index positions in the data. See Split Column Data.