| Excerpt |
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This section describes how to flatten the values in an Array into separate rows 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:
| D trans |
|---|
| RawWrangle | true |
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| Type | step |
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| WrangleText | settype col: Scores type: 'Array' |
|---|
| p01Name | Columns |
|---|
| p01Value | Scores |
|---|
| p02Name | New type |
|---|
| p02Value | Array |
|---|
| SearchTerm | Change column data type |
|---|
|
You can now flatten the Scores column data into separate rows:
| D trans |
|---|
| RawWrangle | true |
|---|
| Type | step |
|---|
| WrangleText | flatten col: Scores |
|---|
| p01Name | Column |
|---|
| p01Value | Scores |
|---|
| SearchTerm | Expand Array into rows |
|---|
|
Results:
| LastName | FirstName | Scores |
|---|
| Adams | Allen | 81 |
| Adams | Allen | 87 |
| Adams | Allen | 83 |
| Adams | Allen | 79 |
| Burns | Bonnie | 98 |
| Burns | Bonnie | 94 |
| Burns | Bonnie | 92 |
| Burns | Bonnie | 85 |
| Cannon | Chris | 88 |
| Cannon | Chris | 81 |
| Cannon | Chris | 85 |
| Cannon | Chris | 78 |
| Tip |
|---|
Tip: You can use aggregation functions on the above data to complete values like average, minimum, and maximum scores. When these aggregation calculations are grouped by student, you can perform the calculations for each student. |
| D s also |
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| label | example_flatten_an_array |
|---|
|
