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.

For a non-conditional 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]


ArgumentRequired?Data TypeDescription
function_col_refYstringName of column to which to apply the function
test_expressionYstring

Expression that is evaluated. Must resolve to true or false

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

function_col_ref

Name of the column the values of which you want to calculate the function. Column must contain Integer or Decimal values.

Required?Data TypeExample Value
YesString (column reference)myValues

test_expression

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

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


Example - MODEIF function

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.

DateRegionOrderCount
1/6/2017r0178
1/6/2017r0297
1/13/2017r0192
1/13/2017r0290
1/20/2017r0197
1/20/2017r0284

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:

Regionmodeif_OrderCount
r0185
r02100