2.6 KiB
Vendored
Computes the sum of values.
null values are ignored.
All primitive numeric types are supported: Byte, Short, Int, Long, Float, and Double.
sum also supports the "mixed" Number type, as long as the column consists only of the aforementioned
primitive numbers.
The numbers are automatically converted to a common type for the operation.
All operations on Double/Float/Number have the skipNaN option, which is
set to false by default. This means that if a NaN is present in the input, it will be propagated to the result.
When it's set to true, NaN values are ignored.
df.sum() // sum of values per every numeric column
df.sum { age and weight } // sum of all values in `age` and `weight`
df.sumFor(skipNaN = true) { age and weight } // sum of values per `age` and `weight` separately
df.sumOf { (weight ?: 0) / age } // sum of expression evaluated for every row
df.age.sum()
df.groupBy { city }.sum()
df.pivot { city }.sum()
df.pivot { city }.groupBy { name.lastName }.sum()
See statistics for details on complex data aggregations.
See column selectors for how to select the columns for this operation.
Type Conversion
The following automatic type conversions are performed for the sum operation:
| Conversion | Result for Empty Input |
|---|---|
| Int -> Int | 0 |
| Byte -> Int | 0 |
| Short -> Int | 0 |
| Long -> Long | 0L |
| Double -> Double | 0.0 |
| Float -> Float | 0.0f |
| Number -> Conversion(Common number type) -> Number | 0.0 |
| Nothing -> Double | 0.0 |