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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.

If a row contains a missing or null value, it is not factored into the calculation. If the entire column contains no values, the function returns a null value.
If there is a tie in which the most occurrences of a value is shared between values, then the lowest value of the evaluated set is returned.
When used in a pivot transform, the function is computed for each instance of the value specified in the group parameter. See Pivot Transform.

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]

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 `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, 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.

Literal values are not supported as inputs.
Multiple columns and wildcards are not supported.

Required?	Data Type	Example Value
Yes	String (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
Yes	String 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.

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