Contents:
NOTE: Transforms are a part of the underlying language, which is not directly accessible to users. This content is maintained for reference purposes only. For more information on the user-accessible equivalent to transforms, see Transformation Reference.
Rows of data are duplicated, once for each input column.
The unpivot column can be applied to multiple columns in the same transform. All columns are un-pivoted into the same key and value columns. When this transform is applied to two columns, the number of rows in the dataset is doubled.
This transform is the opposite of the pivot transform, which converts a set of column values into distinct columns. See Pivot Transform .
Basic Usage
Single- or multi-column example:
You can specify single columns or comma-separated sets of columns.
unpivot col: FirstName, MiddleInitial
Output: Converts the values in the columns FirstName and MiddleInitial into separate key and value columns.
Column range example:
You can also specify ranges of columns using the tilde (~) operator:
unpivot col:Column1~Column20
Output: Converts all of the values in columns between Column1 and Column20 into key and value columns.
Syntax and Parameters
unpivot col: column_ref [groupEvery: int_num]
| Token | Required? | Data Type | Description |
|---|---|---|---|
| unpivot | Y | transform | Name of the transform |
| col | Y | string | Name of source column or columns |
| groupEvery | N | string | If specified, this parameter defines the number of individual key-value pairs to store in each generated column. Default is 1. |
For more information on syntax standards, see Language Documentation Syntax Notes.
col
Identifies the column or columns to which to apply the transform. You can specify one or more columns.
To specify multiple columns:
- Discrete column names are comma-separated.
- Values for column names are case-sensitive.
Column ranges are supported:
myColumn1~myColumn5
NOTE: You can use the asterisk ( * ) wildcard in the Columns textbox to apply the unpivot to the entire dataset, which generates a key and a value column, containing all column-row entries from the source columns. However, unpivoting a large number of columns can significantly increase the number of rows in your dataset.
Usage Notes:
| Required? | Data Type |
|---|---|
| Yes | String (column name) |
groupEvery
This optional parameter is used to create multiple sets of key-value pair columns in the output. The columns listed in the col parameter are placed into each pair of output key-value columns sequentially. After all key-value pair columns are filled in a record, the next column is placed into the first key-value pair column of the next record.
By default, this value is 1, meaning that each column specified in the transform is rendered into a new record in a single pair of key-value columns.
Usage Notes:
| Required? | Data Type |
|---|---|
| No | Integer (positive) |
Tip: For additional examples, see Common Tasks.
Examples
Example - Basic Unpivot
Source:
| productName | productColor | productSize |
|---|---|---|
| Whizbang | red | M |
| Whizbang | red, blue | L |
| Whizbang | green | M |
| Bangwhiz | red | S |
| Bangwhiz | blue | M |
| Bangwhiz | red | S |
Tranformation:
After you have created a header, if necessary, add the following transformation:
| Transformation Name | Unpivot columns |
|---|---|
| Parameter: Columns | productColor |
Results:
| productName | productSize | key | value |
|---|---|---|---|
| Whizbang | M | productColor | red |
| Whizbang | L | productColor | red, blue |
| Whizbang | M | productColor | green |
| Bangwhiz | S | productColor | red |
| Bangwhiz | M | productColor | blue |
| Bangwhiz | S | productColor | red |
Extended:
Note how each instance of a value results in a separate row; duplicate values are included. For a single-column unpivot, this transform results in the same number of rows as the source.
- Since the value is treated as a string, the value
red, blueis treated as one value.
Now, edit the transformation you just added. Replace it with the following, which includes the productSize key as part of the transformation:
| Transformation Name | Unpivot columns |
|---|---|
| Parameter: Columns | productColor,productSize |
Results:
| productName | key | value |
|---|---|---|
| Whizbang | productColor | red |
| Whizbang | productSize | M |
| Whizbang | productColor | red, blue |
| Whizbang | productSize | L |
| Whizbang | productColor | green |
| Whizbang | productSize | M |
| Bangwhiz | productColor | red |
| Bangwhiz | productSize | S |
| Bangwhiz | productColor | blue |
| Bangwhiz | productSize | M |
| Bangwhiz | productColor | red |
| Bangwhiz | productSize | S |
Row keys alternate based on the order in which the source columns are specified in the transform. Since the transform specifies two columns, the number of key-value pairs is doubled, which results in a dataset that has twice as many rows as the source.
Example - Basic Pivot with groupEvery
Tranformation:
From the previous example, modify the unpivot transform to be the following:
Transformation Name
Unpivot columns
Parameter: Columns
productColor,productSize
Parameter: Group size
2
Results:
| productName | key1 | value1 | key2 | value2 |
|---|---|---|---|---|
| Whizbang | productColor | red | productSize | M |
| Whizbang | productColor | red, blue | productSize | L |
| Whizbang | productColor | green | productSize | M |
| Bangwhiz | productColor | red | productSize | S |
| Bangwhiz | productColor | blue | productSize | M |
| Bangwhiz | productColor | red | productSize | S |
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