Computes the mode (most frequent value) from all row values in a column, according to their grouping. Input column can be of Integer, Decimal, or Datetime 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.
modeif(count_visits, health_status == 'sick') 
Output: Returns the mode of the values in the count_visits
column as long as health_status
is set to sick
.
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, Decimal, or Datetime values.
NOTE: If the input is in Datetime type, the output is in unixtime format. You can wrap these outputs in the DATEFORMAT function to generate the results in the appropriate Datetime format. See DATEFORMAT Function. 
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 
Transformation:
To assist, you can first calculate the week number for each row:
Then, you can use the following aggregation to determine the most common order value for each region during the second half of the year:
Results:
Region  modeif_OrderCount 

r01  85 
r02  100 