std

Computes the standard deviation (std, σ) of values.

null values are ignored.

All primitive numeric types are supported: Byte, Short, Int, Long, Float, and Double.

std 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.

The return type is always Double; Double.NaN for empty columns.

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.

Delta Degrees of Freedom: DDoF

All std operations also have the ddof (Delta Degrees of Freedom) argument.

The default is set to 1, meaning DataFrame uses Bessel’s correction to calculate the "unbiased sample standard deviation" by default. This is also the standard in languages like R. However, it is different from the "population standard deviation" (where ddof = 0), which is used in libraries like Numpy.

df.std() // std of values per every numeric column
df.std { age and weight } // std of all values in `age` and `weight`
df.stdFor(skipNaN = true) { age and weight } // std of values per `age` and `weight` separately, skips NA
df.stdOf { (weight ?: 0) / age } // std of expression evaluated for every row

df.std()
df.age.std()
df.groupBy { city }.std()
df.pivot { city }.std()
df.pivot { city }.groupBy { name.lastName }.std()

See statistics for details on complex data aggregations.

Type Conversion

The following automatic type conversions are performed for the mean operation:

Conversion	Result for Empty Input
Int -> Double	Double.NaN
Byte -> Double	Double.NaN
Short -> Double	Double.NaN
Long -> Double	Double.NaN
Double -> Double	Double.NaN
Float -> Double	Double.NaN
Number -> Conversion(Common number type) -> Double	Double.NaN
Nothing -> Double	Double.NaN

20 May 2025