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SOURCEROWNUMBER Function

注記

This function has been superseded by the $sourcerownumber reference. While this function is still usable in the product, it is likely to be deprecated in a future release. Please use $sourcerownumber instead. For more information, see Source Metadata References.

Returns the row number of the current row as it appeared in the original source dataset before any steps had been applied.

The following transforms might make original row information invalid or otherwise unavailable. In these cases, the function returns null values:

  • pivot

  • flatten

  • join

  • lookup

  • union

  • unnest

  • unpivot

注記

If the dataset is sourced from multiple files, a predictable original source row number cannot be guaranteed, and null values are returned.

ヒント

If the source row information is still available, you can hover over the left side of a row in the data grid to see the source row number in the original source data.

Wrangle vs. SQL: This function is part of Wrangle, a proprietary data transformation language. Wrangle is not SQL. For more information, see Wrangle Language.

Basic Usage

Example:

sourcerownumber()

Output: Returns the source row number for each row as it appeared in the original data.Delete Example:

Transformation Name

Filter rows

Parameter: Condition

Custom formula

Parameter: Type of formula

Custom single

Parameter: Condition

sourcerownumber() > 101

Parameter: Action

Delete matching rows

Output: Deletes the rows in the dataset that were after row #101 in the original source data.

Syntax and Arguments

There are no arguments for this function.

Examples

ヒント

For additional examples, see Common Tasks.

Example - Header from row that is not the first one

This example illustrates how you can rename columns based on the contents of specified rows.

Source:

You have imported the following racer data on heat times from a CSV file. When loaded in the Transformer page, it looks like the following:

(rowId)

column2

column3

column4

column5

1

Racer

Heat 1

Heat 2

Heat 3

2

Racer X

37.22

38.22

37.61

3

Racer Y

41.33

DQ

38.04

4

Racer Z

39.27

39.04

38.85

In the above, the (rowId) column references the row numbers displayed in the data grid; it is not part of the dataset. This information is available when you hover over the black dot on the left side of the screen.

Transformation:

You have examined the best performance in each heat according to the sample. You then notice that the data contains headers, but you forget how it was originally sorted. The data now looks like the following:

(rowId)

column2

column3

column4

column5

1

Racer Y

41.33

DQ

38.04

2

Racer

Heat 1

Heat 2

Heat 3

3

Racer X

37.22

38.22

37.61

4

Racer Z

39.27

39.04

38.85

You can use the following transformation to use the third row as your header for each column:

Transformation Name

Rename column with row(s)

Parameter: Option

Use row(s) as column names

Parameter: Type

Use a single row to name columns

Parameter: Row number

3

Results:

After you have applied the above transformation, your data should look like the following:

(rowId)

Racer

Heat_1

Heat_2

Heat_3

3

Racer Y

41.33

DQ

38.04

2

Racer X

37.22

38.22

37.61

4

Racer Z

39.27

39.04

38.85

Example - Using sourcerownumber to create unique row identifiers

The following example demonstrates how to unpack nested data. As part of this example, the SOURCEROWNUMBER function is used as part of a method to create unique row identifiers.

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)

Parameter: Option

Use row(s) as column names

Parameter: Type

Use a single row to name columns

Parameter: Row number

1

Transformation Name

Replace text or pattern

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

New formula

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

New formula

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

New formula

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

New formula

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

Expand arrays to rows

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

Unnest Objects into columns

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

Rename columns

Parameter: Option

Manual rename

Parameter: Column

column_0

Parameter: New column name

'TestNum'

Transformation Name

Rename columns

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

New formula

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

Merge columns

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

Merge columns

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

New formula

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