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You can reshape the row and column structure of your data through a variety of transformations.

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.

Tip: When reshaping your data from its original form, you may find it useful to build your pivots and aggregations as separate recipes created off of your current recipe. In this manner, you can preserve the original structure and explore more significant transformations as needed.

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:

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Reshape your data using pivot tables 

Create Aggregations

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

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 provides a wide range of aggregation functions that you can apply:

  • 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.

Tip: To delete multiple columns, select them in the data grid or column browser. Then select  Delete  from the column menu.

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Reshape your data using Delete columns

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

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p03ValueDelete selected columns
SearchTermDelete columns


Tip: While using Delete columns transformation, you can use the tilde (~) character between the start and end column names to delete a range of columns.

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.