This section describes how to unnest the values in an Array into separate columns in your dataset.
Source:
In the following example dataset, students took the same test three times, and their scores were stored in any array in the Scores
column.
LastName | FirstName | Scores |
---|---|---|
Adams | Allen | [81,87,83,79] |
Burns | Bonnie | [98,94,92,85] |
Cannon | Chris | [88,81,85,78] |
Transformation:
When the data is imported, you might have to re-type the Scores
column as an array:
Transformation Name | Change column data type |
---|---|
Parameter: Columns | Scores |
Parameter: New type | Array |
You can now unnest the Scores
column data into separate columns:
Transformation Name | Unnest Objects into columns |
---|---|
Parameter: Column | Scores |
Parameter: Parameter: Paths to elements | [0] |
Parameter: Parameter: Paths to elements | [1] |
Parameter: Parameter: Paths to elements | [2] |
Parameter: Parameter: Paths to elements | [3] |
Parameter: Remove elements from original | true |
Parameter: Include original column name | true |
In the above transformation:
- Each path is specified in a separate row.
- The
[x]
syntax indicates that the path is the xth element of the array. - The first element of an array is referenced using
[0]
.
- The
- You can choose to delete the element from the original or not. Deleting the element can be a helpful way of debugging your transformation. If all of the elements are gone, then the transformation is complete.
- If you include the original column name in the output column names, you have some contextual information for the outputs.
Results:
LastName | FirstName | Scores_0 | Scores_1 | Scores_2 | Scores_3 |
---|---|---|---|---|---|
Adams | Allen | 81 | 87 | 83 | 79 |
Burns | Bonnie | 98 | 94 | 92 | 85 |
Cannon | Chris | 88 | 81 | 85 | 78 |
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