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# 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 Change column data type Scores Array

You can now flatten the Scores column data into separate rows:

Transformation Name Expand Array into rows 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

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.

## 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 Change column data type Scores Array

You can now unnest the Scores column data into separate columns:

Transformation Name Unnest Objects into columns Scores [0] [1] [2] [3] true 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:

1. One row for each student test

2. 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 Rename column with row(s) Use row(s) as column names Use a single row to name columns 1
Transformation Name Replace text or pattern colScores '\"' '' 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 New formula Single row formula (4 - arraylen(Scores)) '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 New formula Single row formula range(0,arraylen(Scores)) 'Tests'

Also, we will want to create an identifier for the source row using the sourcerownumber function:

Transformation Name New formula Single row formula sourcerownumber() '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 New formula Single row 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 Expand arrays to rows 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 Unnest Objects into columns column1 '[0]','[1]'

After you delete column1, which is no longer needed you should rename the two generated columns:

Transformation Name Rename columns Manual rename column_0 'TestNum'
Transformation Name Rename columns Manual rename column_1 '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 New formula Single row formula (orderIndex * 10) + TestNum 'TestId'

The above are integer values. To make your identifiers look prettier, you might add the following:

Transformation Name Merge 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 Merge columns 'LastName','FirstName' '-' 'studentId'

You can now use this as a grouping parameter for your calculation:

Transformation Name New formula Single row formula average(TestScore) studentId '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 Convert keys/values into Objects Properties {alpha}+ = ','

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 Unnest Objects into columns extractkv_Properties year make model color mileage 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 Extract matches into Array customer_tweets {hashtag} 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"]