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.
- 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 v alue.
- If there is a tie in which the most occurrences of a value is shared between values, then no value is returned from the function.
- When used in a
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.
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.
|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.
For more information on syntax standards, see Language Documentation Syntax Notes.
Name of the column the values of which you want to calculate the function. Column must contain Integer or Decimal values.
- Literal values are not supported as inputs.
- Multiple columns and wildcards are not supported.
|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 |
Example - MODEIF function
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:
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