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.
pivottransform, the function is computed for each instance of the value specified in the
groupparameter. 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.
pivot value:MODEIF(count_visits, health_status == 'sick') group:postal_code limit:1
Output: Generates a two-column 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]
|function_col_ref||Y||string||Name of column to which to apply the function|
Expression that is evaluated. Must resolve to
For more information on the
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)|
This parameter contains the expression to evaluate. This expression must resolve to a Boolean (
|Yes||String expression that evaluates to |
The following data contains a list of weekly orders for 2017 across two regions (
r02). You are interested in calculating the most common order count for the second half of the year, by region.
NOTE: For simplicity, only the first few rows are displayed.
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