Reshapes the data by merging one or more columns into key and value columns. Keys are the names of input columns, and value columns are the cell values from the source. |
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 .
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.
| 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. |
Identifies the column or columns to which to apply the transform. You can specify one or more columns.
| Required? | Data Type |
|---|---|
| Yes | String (column name) |
| Required? | Data Type |
|---|---|
| No | Integer (positive) |
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:
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.
red, blue is 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:
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.
Tranformation:
From the previous example, modify the unpivot transform to be the following:
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 |