SOURCEROWNUMBER Function

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:

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:

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

Use row(s) as column names

Use a single row to name columns

3

Results:

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

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:

Transformation:

When the data is imported from CSV format, you must add a header transform and remove the quotes from the Scores column:

Use row(s) as column names

Use a single row to name columns

1

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:

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:

Single row formula

range(0,arraylen(Scores))

'Tests'

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

Single row formula

sourcerownumber()

'orderIndex'

One row for each student test: Your data should look like the following:

Now, you want to bring together the Tests and Scores arrays into a single nested array using the arrayzip function:

Single row formula

arrayzip([Tests,Scores])

Your dataset has been changed:

Use the following to unpack the nested array:

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:

column1

'[0]','[1]'

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

Manual rename

column_0

'TestNum'

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:

Single row formula

(orderIndex * 10) + TestNum

'TestId'

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

'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:

'LastName','FirstName'

'-'

'studentId'

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

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:

Source:

Your dataset is the following set of orders.

Transformation:

Initially, you want to review your list of orders by last name.

LastName

During your review, you notice that two customer orders are no longer valid and need to be removed. They are:

You might hover over the left side of the screen to reveal the row numbers. You select the row numbers for each of these rows, and a delete suggestion is provided for you. When you click Modify, you see the following transformation:

Custom formula

Custom single

in(sourcerownumber(), [2,7])

Delete matching rows

The above checks the results of the sourcerownumber function, which returns the original row order for the selected rows. If a selected row matches values in the [2,7] array of row numbers, then the row is deleted.

Results:

When the preceding transform is added, your dataset looks like the following, and your sort order is maintained:

Source: