# splitapply

Split data into groups and apply function

## Syntax

## Description

To split data into groups and apply a function to the groups, use the
`findgroups`

and `splitapply`

functions
together. For more information about calculations on groups of data, see Calculations on Groups of Data.

`Y = splitapply(`

splits `func`

,`X`

,`G`

)`X`

into groups specified by `G`

and
applies the function `func`

to each group. Then
`splitapply`

returns `Y`

as an array that
contains the concatenated outputs from `func`

for the groups split
out of `X`

. The input argument `G`

is a vector of
positive integers that specifies the groups to which corresponding elements of
`X`

belong. If `G`

contains
`NaN`

values, `splitapply`

omits the
corresponding values in `X`

when it splits `X`

into groups.

To create `G`

, first use the `findgroups`

function. Then use
`splitapply`

.

`Y = splitapply(`

splits variables of table `func`

,`T`

,`G`

)`T`

into groups, applies
`func`

, and returns `Y`

as an array. The
`splitapply`

function treats the variables of
`T`

as vectors, matrices, or cell arrays, depending on the data
types and sizes of the table variables. If `T`

has
`N`

variables, then `func`

must accept
`N`

input arguments.

`[Y1,...,YM] = splitapply(___)`

splits variables
into groups and applies `func`

to each group.
`func`

returns multiple output arguments.
`Y1,...,YM`

contains the concatenated outputs from
`func`

for the groups split out of the input data variables.
`func`

can return output arguments that belong to different
classes, but the class of each output must be the same each time
`func`

is called. You can use this syntax with any of the input
arguments of the previous syntaxes.

The number of output arguments from `func`

need not be the same
as the number of input arguments specified by `X1,...,XN`

.

## Examples

## Input Arguments

## More About

## Extended Capabilities

## Version History

**Introduced in R2015b**