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rowfun

apply a function to each row of the table/timeseries

Syntax

r = rowfun(f, t)
r = rowfun(f, t, OptionName, Value, ...)

Arguments

f

function to apply with prototype [r1, ..., rN] = f(x1, ..., xN).

t

table or timeseries object in input

Optional pairs OptionName, Value are:

"InputVariables", variable names or variable indices

the function will be apply only on this specified variables.

"GroupingVariables", variable names or variable indices

variables values are used to create unique groups.

"OutputVariableNames", vector of strings

variable names for output table corresponding to the wanted output arguments of the function f. If omitted, the value is generated using 'NumOutputs' ("Var1", ...).

"NumOutputs", real scalar

number of wanted output arguments of the function f. If omitted, the default value is 1 or size of 'OutputVariableNames'.

r

output argument - table or timeseries object (same object as t).

Description

The rowfun function returns a table or timeseries (same object as t) where each row contains the result of the function applied to each row of t.

r = rowfun(f, t) applies the function f to each row of t.

To apply the function only on certain table variables, InputVariables must be specified: r = rowfun(f, t, "InputVariables", ["Var1", ...,]). It is also possible to use the syntax v = rowfun(f, t(:, ["Var1", ...,])).

r = rowfun(f, t, "GroupingVariables", ["Var1", ...,]) allows to specify the variables values are used to create unique groups. The function f will be applied to each group of rows of t.

The function f can have multiple output arguments. r = rowfun(..., NumOutputs, val) allows to specify the number of output arguments. To give a name of output variable, r = rowfun(..., OutputVariableNames, ["VarOutput1", ...]) syntax must be used.

Examples

r = rowfun(f, t)

function z=f(x, y)
    z = x + y;
endfunction

rand("seed", 0)
x1 = floor(rand(5,1)*5)-1.5;
x2 = -floor(rand(5,1)*5)+0.5;
t = table(x1, x2, "VariableNames", ["x1", "x2"])

r = rowfun(f, t)

// With timeseries
timestamp = hours([1 2 2 3 3])';
ts = timeseries(timestamp, x1, x2, "VariableNames", ["timestamp", "x1", "x2"])

r = rowfun(f, ts)

r = rowfun(f, t, "InputVariables", ["Var1", ...])

function z=f(x, y)
    z = x + y;
endfunction

rand("seed", 0)
x1 = floor(rand(5,1)*5)-1.5;
x2 = -floor(rand(5,1)*5)+0.5;
x3 = floor(rand(5,1)*5)+1;
t = table(x1, x2, x3, "VariableNames", ["x1", "x2", "x3"])

r = rowfun(f, t, "InputVariables", ["x1", "x3"])

// Possible with extraction: t(:, ["var1", ..., "varN"])
r = rowfun(f, t(:, ["x1", "x3"]))

// With timeseries
timestamp = hours([1 2 2 3 3])';
ts = timeseries(timestamp, x1, x2, x3, "VariableNames", ["timestamp", "x1", "x2", "x3"])

r = rowfun(f, ts, "InputVariables", ["x1", "x3"])
r = rowfun(f, ts(:, ["x1", "x3"]))

v = rowfun(f, t, "GroupingVariables", ["Var1", ...])

function z=f(x, y)
    z = x + y;
endfunction

rand("seed", 0)
x = ["a"; "b"; "b"; "c"; "a"];
x1 = floor(rand(5,1)*5)-1.5;
x2 = -floor(rand(5,1)*5)+0.5;
x3 = floor(rand(5,1)*5)+1;
t = table(x, x1, x2, "VariableNames", ["x", "x1", "x2"])

r = rowfun(f, t, "GroupingVariables", "x")

t.x3 = x3;
r = rowfun(f, t, "GroupingVariables", "x", "InputVariables", ["x1", "x3"])

// With timeseries
timestamp = hours([1 2 2 3 3])';
ts = timeseries(timestamp, x1, x2, "VariableNames", ["timestamp", "x1", "x2"])

r = rowfun(f, ts, "GroupingVariables", "timestamp")

v = rowfun(..., "OutputVariableNames", ["Var1", ...])

function z=f(x, y)
    z = x + y;
endfunction

rand("seed", 0)
x = ["a"; "b"; "b"; "c"; "a"];
x1 = floor(rand(5,1)*5)-1.5;
x2 = -floor(rand(5,1)*5)+0.5;
t = table(x, x1, x2, "VariableNames", ["x", "x1", "x2"])

r = rowfun(f, t, "GroupingVariables", "x", "OutputVariableNames", "result")

// With timeseries
timestamp = hours([1 2 2 3 3])';
ts = timeseries(timestamp, x1, x2, "VariableNames", ["timestamp", "x1", "x2"])

r = rowfun(f, ts, "GroupingVariables", "timestamp", "OutputVariableNames", "result")

function [z1, z2, z3]=f(x, y)
    z1 = x + y;
    z2 = x - y;
    z3 = x.^2 + y.^2;
endfunction

rand("seed", 0)
x1 = floor(rand(5,1)*5)-1.5;
x2 = -floor(rand(5,1)*5)+0.5;
t = table(x1, x2, "VariableNames", ["x1", "x2"])

r = rowfun(f, t, "OutputVariableNames", ["a", "b", "c"])
r = rowfun(f, t, "NumOutputs", 3)

// With timeseries
timestamp = hours([1 2 2 3 3])';
ts = timeseries(timestamp, x1, x2, "VariableNames", ["timestamp", "x1", "x2"])

r = rowfun(f, t, "OutputVariableNames", ["a", "b", "c"])
r = rowfun(f, t, "NumOutputs", 3)

See also

History

VersionDescription
2024.0.0 Introduction in Scilab.

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