Computes the mode (most frequent value) from all row values in a column, according to their grouping. Input column can be of Integer or Decimal type. 
pivot
transform, the function is computed for each instance of the value specified in the group
parameter. See Pivot Transform. For a nonconditional version of this function, see MODE Function.
For a version of this function computed over a rolling window of rows, see ROLLINGMODE Function.
pivot value:MODEIF(count_visits, health_status == 'sick') group:postal_code limit:1 
Output: Generates a twocolumn table containing the unique values from the postal_code
column and the mode of the values in the count_visits
column as long as health_status
is set to sick
, for the postal_code
value. The limit
parameter defines the maximum number of output columns.
pivot value:MODEIF(function_col_ref, test_expression) [group:group_col_ref] [limit:limit_count] 
Argument  Required?  Data Type  Description 

function_col_ref  Y  string  Name of column to which to apply the function 
test_expression  Y  string  Expression that is evaluated. Must resolve to 
For more information on the group
and limit
parameters, see Pivot Transform.
Name of the column the values of which you want to calculate the function. Column must contain Integer or Decimal values.
Required?  Data Type  Example Value 

Yes  String (column reference)  myValues

This parameter contains the expression to evaluate. This expression must resolve to a Boolean (true
or false
) value.
Required?  Data Type  Example Value 

Yes  String expression that evaluates to true or false  (LastName == 'Mouse' && FirstName == 'Mickey') 
The following data contains a list of weekly orders for 2017 across two regions (r01
and r02
). You are interested in calculating the most common order count for the second half of the year, by region.
Source:
NOTE: For simplicity, only the first few rows are displayed. 
Date  Region  OrderCount 

1/6/2017  r01  78 
1/6/2017  r02  97 
1/13/2017  r01  92 
1/13/2017  r02  90 
1/20/2017  r01  97 
1/20/2017  r02  84 
Transform:
To assist, you can first calculate the week number for each row:
derive type: single value: WEEKNUM(Date) as: 'weekNumber' 
Then, you can use the following aggregation to determine the most common order value for each region during the second half of the year:
pivot group: Region value: MODEIF(OrderCount, weekNumber > 26) limit: 50 
Results:
Region  modeif_OrderCount 

r01  85 
r02  100 