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 
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  (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 nonnull 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 


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 citywide 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 


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.