Unnest Your Data
You can unnest Array or Object values into separate rows or columns using the following transformations.
Flatten Array Values into Rows
Array values can be flattened into individual values in separate rows.
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.
Unnest Array Values into New Columns
You can also split out the individual values in an array into separate columns.
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 | |
---|---|
Parameter: Columns | Scores |
Parameter: New type | Array |
You can now unnest the Scores
column data into separate columns:
Transformation Name | |
---|---|
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]
.
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 |
Flatten and Unnest Together
The following example illustrates how flatten and unnest can be used together to reshape your data.
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 |
Unnest Object Values into New Columns
This example shows how you can unnest Object data into separate columns. The example contains vehicle identifiers, and the Properties
column contains key-value pairs describing characteristics of each vehicle.
This example shows how you can unpack data nested in an Object into separate columns.
Source:
You have the following information on used cars. The VIN
column contains vehicle identifiers, and the Properties
column contains key-value pairs describing characteristics of each vehicle. You want to unpack this data into separate columns.
VIN | Properties |
---|---|
XX3 JT4522 | year=2004,make=Subaru,model=Impreza,color=green,mileage=125422,cost=3199 |
HT4 UJ9122 | year=2006,make=VW,model=Passat,color=silver,mileage=102941,cost=4599 |
KC2 WZ9231 | year=2009,make=GMC,model=Yukon,color=black,mileage=68213,cost=12899 |
LL8 UH4921 | year=2011,make=BMW,model=328i,color=brown,mileage=57212,cost=16999 |
Transformation:
Add the following transformation, which identifies all of the key values in the column as beginning with alphabetical characters.
The
valueafter
string identifies where the corresponding value begins after the key.The
delimiter
string indicates the end of each key-value pair.
Transformation Name | |
---|---|
Parameter: Column | Properties |
Parameter: Key | `{alpha}+` |
Parameter: Separator between key and value | `=` |
Parameter: Delimiter between pair | ',' |
Now that the Object of values has been created, you can use the unnest
transform to unpack this mapped data. In the following, each key is specified, which results in separate columns headed by the named key:
Note
Each key must be entered on a separate line in the Path to elements area.
Transformation Name | |
---|---|
Parameter: Column | extractkv_Properties |
Parameter: Paths to elements | year |
Parameter: Paths to elements | make |
Parameter: Paths to elements | model |
Parameter: Paths to elements | color |
Parameter: Paths to elements | mileage |
Parameter: Paths to elements | cost |
Results:
When you delete the unnecessary Properties columns, the dataset now looks like the following:
VIN | year | make | model | color | mileage | cost |
---|---|---|---|---|---|---|
XX3 JT4522 | 2004 | Subaru | Impreza | green | 125422 | 3199 |
HT4 UJ9122 | 2006 | VW | Passat | silver | 102941 | 4599 |
KC2 WZ9231 | 2009 | GMC | Yukon | black | 68213 | 12899 |
LL8 UH4921 | 2011 | BMW | 328i | brown | 57212 | 16999 |
Extract a Set of Values
This example shows how to extract values (for example, hashtag values) from a column and convert them into a column of arrays.
In this example, you extract one or more values from a source column and assemble them in an Array column.
Suppose you need to extract the hashtags from customer tweets to another column. In such cases, you can use the {hashtag}
Alteryx pattern to extract all hashtag values from a customer's tweets into a new column.
Source:
The following dataset contains customer tweets across different locations.
User Name | Location | Customer tweets |
---|---|---|
James | U.K | Excited to announce that we’ve transitioned Wrangler from a hybrid desktop application to a completely cloud-based service! #dataprep #businessintelligence #CommitToCleanData # London |
Mark | Berlin | Learnt more about the importance of identifying issues in your data—early and often #CommitToCleanData #predictivetransformations #realbusinessintelligence |
Catherine | Paris | Clean data is the foundation of your analysis. Learn more about what we consider the five tenets of sound #dataprep, starting with #1a prioritizing and setting targets. #startwiththeuser #realbusinessintelligence #Paris |
Dave | New York | Learn how #NewYorklife onboarded as part of their #bigdata #dataprep initiative to unlock hidden insights and make them accessible across departments. |
Christy | San Francisco | How can you quickly determine the number of times a user ID appears in your data?#dataprep #pivot #aggregation#machinelearning initiatives #SFO |
Transformation:
The following transformation extracts the hashtag messages from customer tweets.
Transformation Name | |
---|---|
Parameter: Column | customer_tweets |
Parameter: Pattern matching elements in the list | `{hashtag}` |
Parameter: New column name | Hashtag tweets |
Then, the source column can be deleted.
Results:
User Name | Location | Hashtag tweets |
---|---|---|
James | U.K | ["#dataprep", "#businessintelligence", "#CommitToCleanData", " # London"] |
Mark | Berlin | ["#CommitToCleanData", "#predictivetransformations", "#realbusinessintelligence", "0"] |
Catherine | Paris | ["#dataprep", "#startwiththeuser","#realbusinessintelligence", "# Paris"] |
Dave | New York | ["#NewYorklife", "dataprep", "bigdata", "0"] |
Christy | SanFrancisco | [ "dataprep", "#pivot", "#aggregation", "#machinelearning"] |