Flatten 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.
Unpacks array data into separate rows for each value. This transform operates on a single column.
This transform does not reference keys in the array. If your array data contains keys, use the unnest
transform. See Unnest Transform.
Basic Usage
flatten col: myArray
Output: Generates a separate row for each value in the array. Values of other columns in generated rows are copied from the source.
Syntax and Parameters
flatten: col: column_ref
Token | Required? | Data Type | Description |
---|---|---|---|
flatten | Y | transform | Name of the transform |
col | Y | string | Source column name |
For more information on syntax standards, see Language Documentation Syntax Notes.
col
Identifies the column to which to apply the transform. You can specify only one column.
Usage Notes:
Required? | Data Type |
---|---|
Yes | String (column name) |
Examples
Astuce
For additional examples, see Common Tasks.
Example - Flatten an array
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:
Transformation Name | |
---|---|
Parameter: Columns | Scores |
Parameter: New type | Array |
You can now flatten the Scores
column data into separate rows:
Transformation Name | |
---|---|
Parameter: Column | Scores |
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 |
Astuce
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.
Example - Flatten and unnest together
While the above example nicely flattens out your data, there are two potential problems with the results:
There is no identifier for each test. For example, Allen Adams' score of 87 cannot be associated with the specific test on which he recorded the score.
There is no unique identifier for each row.
The following example addresses both of these issues. It also demonstrates differences between the unnest
and the flatten
transform, including how you use unnest
to flatten array data based on specified keys.
For more information, see Unnest Transform.
This example illustrates you to use the flatten and unnest transforms.
Source:
You have the following data on student test scores. Scores on individual scores are stored in the Scores
array, and you need to be able to track each test on a uniquely identifiable row. This example has two goals:
One row for each student test
Unique identifier for each student-score combination
LastName | FirstName | Scores |
---|---|---|
Adams | Allen | [81,87,83,79] |
Burns | Bonnie | [98,94,92,85] |
Cannon | Charles | [88,81,85,78] |
Transformation:
When the data is imported from CSV format, you must add a header
transform and remove the quotes from the Scores
column:
Transformation Name | |
---|---|
Parameter: Option | Use row(s) as column names |
Parameter: Type | Use a single row to name columns |
Parameter: Row number | 1 |
Transformation Name | |
---|---|
Parameter: Column | colScores |
Parameter: Find | '\"' |
Parameter: Replace with | '' |
Parameter: Match all occurrences | true |
Validate test date: To begin, you might want to check to see if you have the proper number of test scores for each student. You can use the following transform to calculate the difference between the expected number of elements in the Scores
array (4) and the actual number:
Transformation Name | |
---|---|
Parameter: Formula type | Single row formula |
Parameter: Formula | (4 - arraylen(Scores)) |
Parameter: New column name | 'numMissingTests' |
When the transform is previewed, you can see in the sample dataset that all tests are included. You might or might not want to include this column in the final dataset, as you might identify missing tests when the recipe is run at scale.
Unique row identifier: The Scores
array must be broken out into individual rows for each test. However, there is no unique identifier for the row to track individual tests. In theory, you could use the combination of LastName-FirstName-Scores
values to do so, but if a student recorded the same score twice, your dataset has duplicate rows. In the following transform, you create a parallel array called Tests
, which contains an index array for the number of values in the Scores
column. Index values start at 0
:
Transformation Name | |
---|---|
Parameter: Formula type | Single row formula |
Parameter: Formula | range(0,arraylen(Scores)) |
Parameter: New column name | 'Tests' |
Also, we will want to create an identifier for the source row using the sourcerownumber
function:
Transformation Name | |
---|---|
Parameter: Formula type | Single row formula |
Parameter: Formula | sourcerownumber() |
Parameter: New column name | 'orderIndex' |
One row for each student test: Your data should look like the following:
LastName | FirstName | Scores | Tests | orderIndex |
---|---|---|---|---|
Adams | Allen | [81,87,83,79] | [0,1,2,3] | 2 |
Burns | Bonnie | [98,94,92,85] | [0,1,2,3] | 3 |
Cannon | Charles | [88,81,85,78] | [0,1,2,3] | 4 |
Now, you want to bring together the Tests
and Scores
arrays into a single nested array using the arrayzip
function:
Transformation Name | |
---|---|
Parameter: Formula type | Single row formula |
Parameter: Formula | arrayzip([Tests,Scores]) |
Your dataset has been changed:
LastName | FirstName | Scores | Tests | orderIndex | column1 |
---|---|---|---|---|---|
Adams | Allen | [81,87,83,79] | [0,1,2,3] | 2 | [[0,81],[1,87],[2,83],[3,79]] |
Adams | Bonnie | [98,94,92,85] | [0,1,2,3] | 3 | [[0,98],[1,94],[2,92],[3,85]] |
Cannon | Charles | [88,81,85,78] | [0,1,2,3] | 4 | [[0,88],[1,81],[2,85],[3,78]] |
Use the following to unpack the nested array:
Transformation Name | |
---|---|
Parameter: Column | column1 |
Each test-score combination is now broken out into a separate row. The nested Test-Score combinations must be broken out into separate columns using the following:
Transformation Name | |
---|---|
Parameter: Column | column1 |
Parameter: Paths to elements | '[0]','[1]' |
After you delete column1
, which is no longer needed you should rename the two generated columns:
Transformation Name | |
---|---|
Parameter: Option | Manual rename |
Parameter: Column | column_0 |
Parameter: New column name | 'TestNum' |
Transformation Name | |
---|---|
Parameter: Option | Manual rename |
Parameter: Column | column_1 |
Parameter: New column name | 'TestScore' |
Unique row identifier: You can do one more step to create unique test identifiers, which identify the specific test for each student. The following uses the original row identifier OrderIndex
as an identifier for the student and the TestNumber
value to create the TestId
column value:
Transformation Name | |
---|---|
Parameter: Formula type | Single row formula |
Parameter: Formula | (orderIndex * 10) + TestNum |
Parameter: New column name | 'TestId' |
The above are integer values. To make your identifiers look prettier, you might add the following:
Transformation Name | |
---|---|
Parameter: Columns | 'TestId00','TestId' |
Extending: You might want to generate some summary statistical information on this dataset. For example, you might be interested in calculating each student's average test score. This step requires figuring out how to properly group the test values. In this case, you cannot group by the LastName
value, and when executed at scale, there might be collisions between first names when this recipe is run at scale. So, you might need to create a kind of primary key using the following:
Transformation Name | |
---|---|
Parameter: Columns | 'LastName','FirstName' |
Parameter: Separator | '-' |
Parameter: New column name | 'studentId' |
You can now use this as a grouping parameter for your calculation:
Transformation Name | |
---|---|
Parameter: Formula type | Single row formula |
Parameter: Formula | average(TestScore) |
Parameter: Group rows by | studentId |
Parameter: New column name | 'avg_TestScore' |
Results:
After you delete unnecessary columns and move your columns around, the dataset should look like the following:
TestId | LastName | FirstName | TestNum | TestScore | studentId | avg_TestScore |
---|---|---|---|---|---|---|
TestId0021 | Adams | Allen | 0 | 81 | Adams-Allen | 82.5 |
TestId0022 | Adams | Allen | 1 | 87 | Adams-Allen | 82.5 |
TestId0023 | Adams | Allen | 2 | 83 | Adams-Allen | 82.5 |
TestId0024 | Adams | Allen | 3 | 79 | Adams-Allen | 82.5 |
TestId0031 | Adams | Bonnie | 0 | 98 | Adams-Bonnie | 92.25 |
TestId0032 | Adams | Bonnie | 1 | 94 | Adams-Bonnie | 92.25 |
TestId0033 | Adams | Bonnie | 2 | 92 | Adams-Bonnie | 92.25 |
TestId0034 | Adams | Bonnie | 3 | 85 | Adams-Bonnie | 92.25 |
TestId0041 | Cannon | Chris | 0 | 88 | Cannon-Chris | 83 |
TestId0042 | Cannon | Chris | 1 | 81 | Cannon-Chris | 83 |
TestId0043 | Cannon | Chris | 2 | 85 | Cannon-Chris | 83 |
TestId0044 | Cannon | Chris | 3 | 78 | Cannon-Chris | 83 |