Unpivot Transform
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.
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 thepivottransform, which converts a set of column values into distinct columns. SeePivot 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 |
For more information on syntax standards, see Language Documentation Syntax Notes.
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
For the col value, you can use the asterisk ( * ) wildcard 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) |
Specifies the number of output key-value pair columns to produce after unpivoting.
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) |
Examples
Tip
For additional examples, see Common Tasks.
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 |
|
|---|---|
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 |
|
|---|---|
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.
Tranformation:
From the previous example, modify the unpivot transform to be the following:
Transformation Name |
|
|---|---|
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 |