SUMIF Function

Generates the sum of rows in each group that meet a specific condition.

Note

When added to a transform, this function is applied to the sample in the data grid. If you change your sample or run the job, the computed values for this function are updated. Transforms that change the number of rows in subsequent recipe steps do not affect the values computed for this step.

To perform a simple summing of rows without conditionals, use the SUM function. See SUM Function.

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

sumif(timeoutSecs, errors >= 1)

Output: Returns the sum of the timeoutSecs column when the errors value is greater than or equal to 1.

Syntax and Arguments

sumif(col_ref, test_expression) [group:group_col_ref] [limit:limit_count]

Argument	Required?	Data Type	Description
col_ref	Y	string	Reference to the column you wish to evaluate.
test_expression	Y	string	Expression that is evaluated. Must resolve to `true` or `false`

For more information on syntax standards, see Language Documentation Syntax Notes.

For more information on the group and limit parameters, see Pivot Transform.

col_ref

Name of the column whose values you wish to use in the calculation. Column must be a numeric (Integer or Decimal) type.

Usage Notes:

Required?	Data Type	Example Value
Yes	String that corresponds to the name of the column	myValues

test_expression

This parameter contains the expression to evaluate. This expression must resolve to a Boolean (true or false) value.

Usage Notes:

Required?	Data Type	Example Value
Yes	String expression that evaluates to `true` or `false`	(LastName == 'Mouse' && FirstName == 'Mickey')

Examples

Tip

For additional examples, see Common Tasks.

Example - Summarize Voter Registrations

This example illustrates how you can use conditional calculation functions.

Functions:

Item	Description
SUMIF Function	Generates the sum of rows in each group that meet a specific condition.
COUNTDISTINCTIF Function	Generates the count of distinct non-null values for rows in each group that meet a specific condition.

Source:

Here is some example polling data across 16 precincts in 8 cities across 4 counties, where registrations have been invalidated at the polling station, preventing voters from voting. Precincts where this issue has occurred previously have been added to a watch list (precinctWatchList).

totalReg	invalidReg	precinctWatchList	precinctId	cityId	countyId
731	24	y	1	1	1
743	29	y	2	1	1
874	0		3	2	1
983	0		4	2	1
622	29		5	3	2
693	0		6	3	2
775	37	y	7	4	2
1025	49	y	8	4	2
787	13		9	5	3
342	0		10	5	3
342	39	y	11	6	3
387	28	y	12	6	3
582	59		13	7	4
244	0		14	7	4
940	6	y	15	8	4
901	4	y	16	8	4

Transformation:

First, you want to sum up the invalid registrations (invalidReg) for precincts that are already on the watchlist (precinctWatchList = y). These sums are grouped by city, which can span multiple precincts:

Transformation Name	`New formula`
Parameter: Formula type	Single row formula
Parameter: Formula	SUMIF(invalidReg, precinctWatchList == "y")
Parameter: Group rows by	cityId
Parameter: New column name	'invalidRegbyCityId'

The invalidRegbyCityId column contains invalid registrations across the entire city.

Now, at the county level, you want to identify the number of precincts that were on the watch list and were part of a city-wide registration problem.

In the following step, the number of cities in each count are counted where invalid registrations within a city is greater than 60.

This step creates a pivot aggregation.

Transformation Name	`Pivot columns`
Parameter: Row labels	countyId
Parameter: Values	COUNTDISTINCTIF(precinctId, invalidRegbyCityId > 60)
Parameter: Max number of columns to create	1

Results:

countyId	countdistinctif_precinctId
1	0
2	2
3	2
4	0

The voting officials in counties 2 and 3 should investigate their precinct registration issues.

In this section: