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.
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.
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
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, 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)|
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:
Then, you can use the following aggregation to determine the most common order value for each region during the second half of the year: