# Averages/Arithmetic mean

You are encouraged to solve this task according to the task description, using any language you may know.

Write a program to find the mean (arithmetic average) of a numeric vector.

In case of a zero-length input, since the mean of an empty set of numbers is ill-defined, the program may choose to behave in any way it deems appropriate, though if the programming language has an established convention for conveying math errors or undefined values, it's preferable to follow it.

## 0815

```{x{+=<:2:x/%<:d:~\$<:01:~><:02:~><:03:~><:04:~><:05:~><:06:~><:07:~><:08:
~><:09:~><:0a:~><:0b:~><:0c:~><:0d:~><:0e:~><:0f:~><:10:~><:11:~><:12:~>
<:13:~><:14:~><:15:~><:16:~><:17:~><:18:~><:19:~><:ffffffffffffffff:~>{x
{+>}:8f:{&={+>{~>&=x<:ffffffffffffffff:/#:8f:{{=<:19:x/%```
Output:
```0
D
```

## 11l

Translation of: Python
```F average(x)
R sum(x) / Float(x.len)

print(average([0, 0, 3, 1, 4, 1, 5, 9, 0, 0]))```
Output:
```2.3
```

## 360 Assembly

Compact and functional.

```AVGP     CSECT
USING  AVGP,12
LR     12,15
SR     3,3                i=0
SR     6,6                sum=0
LOOP     CH     3,=AL2(NN-T-1)     for i=1 to nn
BH     ENDLOOP
L      2,T(3)             t(i)
MH     2,=H'100'          scaling factor=2
AR     6,2                sum=sum+t(i)
LA     3,4(3)             next i
B      LOOP
ENDLOOP  LR     5,6                sum
LA     4,0
D      4,NN               sum/nn
XDECO  5,Z                edit binary
MVC    U,Z+10             descale
MVI    Z+10,C'.'
MVC    Z+11(2),U
XPRNT  Z,80               output
XR     15,15
BR     14
T        DC     F'10',F'9',F'8',F'7',F'6',F'5',F'4',F'3',F'2',F'1'
NN       DC     A((NN-T)/4)
Z        DC     CL80' '
U        DS     CL2
END    AVGP```
Output:
`         5.50`

## 6502 Assembly

Called as a subroutine (i.e., JSR ArithmeticMean), this calculates the integer average of up to 255 8-bit unsigned integers. The address of the beginning of the list of integers is in the memory location ArrayPtr and the number of integers is in the memory location NumberInts. The arithmetic mean is returned in the memory location ArithMean.

```ArithmeticMean:		PHA
TYA
PHA		;push accumulator and Y register onto stack

LDA #0
STA Temp
STA Temp+1	;temporary 16-bit storage for total

LDY NumberInts
BEQ Done	;if NumberInts = 0 then return an average of zero

CLC
STA Temp
LDA Temp+1
STA Temp+1
DEY
CPY #255

LDY #-1
DivideLoop:		LDA Temp
SEC
SBC NumberInts
STA Temp
LDA Temp+1
SBC #0
STA Temp+1
INY
BCS DivideLoop

Done:			STY ArithMean	;store result here
PLA		;restore accumulator and Y register from stack
TAY
PLA
RTS		;return from routine```

## 8th

```: avg \ a -- avg(a)
dup ' n:+ 0 a:reduce
swap a:len nip n:/ ;

\ test:
[ 1.0, 2.3, 1.1, 5.0, 3, 2.8, 2.01, 3.14159 ] avg . cr
[ ] avg . cr
[ 10 ] avg . cr
bye
```

Output is:
2.54395
NaN
10.00000

## ACL2

```(defun mean-r (xs)
(if (endp xs)
(mv 0 0)
(mv-let (m j)
(mean-r (rest xs))
(mv (+ (first xs) m) (+ j 1)))))

(defun mean (xs)
(if (endp xs)
0
(mv-let (n d)
(mean-r xs)
(/ n d))))
```

## Action!

```INCLUDE "D2:REAL.ACT" ;from the Action! Tool Kit

PROC Mean(INT ARRAY a INT count REAL POINTER result)
INT i
REAL x,sum,tmp

IntToReal(0,sum)
FOR i=0 TO count-1
DO
IntToReal(a(i),x)
RealAssign(tmp,sum)
OD
IntToReal(count,tmp)
RealDiv(sum,tmp,result)
RETURN

PROC Test(INT ARRAY a INT count)
INT i
REAL result

Mean(a,count,result)
Print("mean(")
FOR i=0 TO count-1
DO
PrintI(a(i))
IF i<count-1 THEN
Put(',)
FI
OD
Print(")=")
PrintRE(result)
RETURN

PROC Main()
INT ARRAY a1=[1 2 3 4 5 6]
INT ARRAY a2=[1 10 100 1000 10000]
INT ARRAY a3=

Put(125) PutE() ;clear screen
Test(a1,6)
Test(a2,5)
Test(a3,1)
Test(a3,0)
RETURN```
Output:
```mean(1,2,3,4,5,6)=3.5
mean(1,10,100,1000,10000)=2222.2
mean(9)=9
mean()=0
```

## ActionScript

```function mean(vector:Vector.<Number>):Number
{
var sum:Number = 0;
for(var i:uint = 0; i < vector.length; i++)
sum += vector[i];
return vector.length == 0 ? 0 : sum / vector.length;
}
```

This example shows how to pass a zero length vector as well as a larger vector. With Ada 2012 it is possible to check that pre conditions are satisfied (otherwise an exception is thrown). So we check that the length is not zero.

```with Ada.Float_Text_Io; use Ada.Float_Text_Io;

procedure Mean_Main is
type Vector is array (Positive range <>) of Float;
function Mean (Item : Vector) return float with pre => Item'length > 0;
function Mean (Item : Vector) return Float is
Sum : Float := 0.0;
begin
for I in Item'range loop
Sum := Sum + Item(I);
end loop;
return Sum / Float(Item'Length);
end Mean;
A : Vector := (3.0, 1.0, 4.0, 1.0, 5.0, 9.0);
begin
Put(Item => Mean (A), Fore => 1, Exp => 0);
New_Line;
-- test for zero length vector
Put(Item => Mean(A (1..0)), Fore => 1, Exp => 0);
New_Line;
end Mean_Main;
```

Output: 3.83333

raised SYSTEM.ASSERTIONS.ASSERT_FAILURE : failed precondition from mean_main.adb:6

## Aime

```real
mean(list l)
{
real sum, x;

sum = 0;
for (, x in l) {
sum += x;
}

sum / ~l;
}

integer
main(void)
{
o_form("%f\n", mean(list(4.5, 7.25, 5r, 5.75)));

0;
}```

## ALGOL 68

Translation of: C
Works with: ALGOL 68 version Standard - no extensions to language used
Works with: ALGOL 68G version Any - tested with release mk15-0.8b.fc9.i386
Works with: ELLA ALGOL 68 version Any (with appropriate job cards) - tested with release 1.8.8d.fc9.i386 - note that some necessary LONG REAL operators are missing from ELLA's library.
```PROC mean = (REF[]REAL p)REAL:
# Calculates the mean of qty REALs beginning at p. #
IF LWB p > UPB p THEN 0.0
ELSE
REAL total := 0.0;
FOR i FROM LWB p TO UPB p DO total +:= p[i] OD;
total / (UPB p - LWB p + 1)
FI;

main:(
REAL test := (1.0, 2.0, 5.0, -5.0, 9.5, 3.14159);
print((mean(test),new line))
)```

## ALGOL W

```begin
% procedure to find the mean of the elements of a vector.                %
% As the procedure can't find the bounds of the array for itself,        %
% we pass them in lb and ub          %
real procedure mean ( real    array vector ( * )
; integer value lb
; integer value ub
) ;
begin
real sum;
assert( ub > lb ); % terminate the program if there are no elements  %
sum := 0;
for i := lb until ub do sum := sum + vector( i );
sum / ( ( ub + 1 ) - lb )
end mean ;

% test the mean procedure by finding the mean of 1.1, 2.2, 3.3, 4.4, 5.5 %
real array numbers ( 1 :: 5 );
for i := 1 until 5 do numbers( i ) := i + ( i / 10 );
r_format := "A"; r_w := 10; r_d := 2; % set fixed point output           %
write( mean( numbers, 1, 5 ) );
end.```

## AmigaE

Because of the way Amiga E handles floating point numbers, the passed list/vector must contain all explicitly floating point values (e.g., you need to write "1.0", not "1")

```PROC mean(l:PTR TO LONG)
DEF m, i, ll
ll := ListLen(l)
IF ll = 0 THEN RETURN 0.0
m := 0.0
FOR i := 0 TO ll-1 DO m := !m + l[i]
m := !m / (ll!)
ENDPROC m

PROC main()
DEF s : STRING
WriteF('mean \s\n',
RealF(s,mean([1.0, 2.0, 3.0, 4.0, 5.0]), 2))
ENDPROC```

## AntLang

AntLang has a built-in avg function.

`avg[list]`

## APL

Works with: APL2
```      X←3 1 4 1 5 9
(+/X)÷⍴X
3.833333333
```

## AppleScript

### Vanilla

With vanilla AppleScript, the process is the literal one of adding the numbers and dividing by the list length. It naturally returns results of class real, but it would be simple to return integer-representable results as integers if required.

```on average(listOfNumbers)
set len to (count listOfNumbers)
if (len is 0) then return missing value

set sum to 0
repeat with thisNumber in listOfNumbers
set sum to sum + thisNumber
end repeat

return sum / len
end average

average({2500, 2700, 2400, 2300, 2550, 2650, 2750, 2450, 2600, 2400})
```
Output:
`2530.0`

### ASObjC

The vanilla method above is the more efficient with lists of up to around 100 numbers. But for longer lists, using Foundation methods with AppleScriptObjectC can be useful

```use AppleScript version "2.4" -- OS X 10.10 (Yosemite) or later
use framework "Foundation"

on average(listOfNumbers)
if ((count listOfNumbers) is 0) then return missing value

set arrayOfNumbers to current application's class "NSArray"'s arrayWithArray:(listOfNumbers)
return (arrayOfNumbers's valueForKeyPath:("@avg.self")) as real
end average

average({2500, 2700, 2400, 2300, 2550, 2650, 2750, 2450, 2600, 2400})
```
Output:
`2530.0`

## Applesoft BASIC

```REM COLLECTION IN DATA STATEMENTS, EMPTY DATA IS THE END OF THE COLLECTION
1 IF LEN(V\$) = 0 THEN END
2 N = 0
3 S = 0
4 FOR I = 0 TO 1 STEP 0
5     S = S + VAL(V\$)
6     N = N + 1
8     IF LEN(V\$) THEN NEXT
9 PRINT S / N
10000 DATA1,2,2.718,3,3.142
63999 DATA

REM COLLECTION IN AN ARRAY, ITEM 0 IS THE SIZE OF THE COLLECTION
A(0) = 5 : A(1) = 1 : A(2) = 2 : A(3) = 2.718 : A(4) = 3 : A(5) = 3.142
N = A(0) : IF N THEN S = 0 : FOR I = 1 TO N : S = S + A(I) : NEXT : ? S / N```

## Arturo

```arr: [1 2 3 4 5 6 7]

print average arr
```
Output:
`4.0`

## Astro

```mean([1, 2, 3])
mean(1..10)
mean([])```

## AutoHotkey

```i = 10
Loop, % i {
Random, v, -3.141592, 3.141592
list .= v "`n"
sum += v
}
MsgBox, % i ? list "`nmean: " sum/i:0
```

## AWK

```cat mean.awk
#!/usr/local/bin/gawk -f

# User defined function
function mean(v,      i,n,sum) {
for (i in v) {
n++
sum += v[i]
}
if (n>0) {
return(sum/n)
} else {
return("zero-length input !")
}
}

BEGIN {
# fill a vector with random numbers
for(i=0; i < 10; i++) {
vett[i] = rand()*10
}
print mean(vett)
print mean(nothing)
}
```
Output:
```\$ awk -f mean.awk
3.92689
zero-length input !
```

## Babel

`(3 24 18 427 483 49 14 4294 2 41) dup len <- sum ! -> / itod <<`
Output:
`535`

## BASIC

Works with: QBasic

Assume the numbers are in an array named "nums".

```mean = 0
sum = 0;
FOR i = LBOUND(nums) TO UBOUND(nums)
sum = sum + nums(i);
NEXT i
size = UBOUND(nums) - LBOUND(nums) + 1
PRINT "The mean is: ";
IF size <> 0 THEN
PRINT (sum / size)
ELSE
PRINT 0
END IF
```

### BBC BASIC

To calculate the mean of an array:

```      REM specific functions for the array/vector types

REM Byte Array
DEF FN_Mean_Arithmetic&(n&())
= SUM(n&()) / (DIM(n&(),1)+1)

REM Integer Array
DEF FN_Mean_Arithmetic%(n%())
= SUM(n%()) / (DIM(n%(),1)+1)

REM Float 40 array
DEF FN_Mean_Arithmetic(n())
= SUM(n()) / (DIM(n(),1)+1)

REM A String array
DEF FN_Mean_Arithmetic\$(n\$())
LOCAL I%, S%, sum, Q%
S% = DIM(n\$(),1)
FOR I% = 0 TO S%
Q% = TRUE
ON ERROR LOCAL Q% = FALSE
IF Q% sum += EVAL(n\$(I%))
NEXT
= sum / (S%+1)

REM Float 64 array
DEF FN_Mean_Arithmetic#(n#())
= SUM(n#()) / (DIM(n#(),1)+1)```

Michael Hutton 14:02, 29 May 2011 (UTC)

### IS-BASIC

```100 NUMERIC ARR(3 TO 8)
110 LET ARR(3)=3:LET ARR(4)=1:LET ARR(5)=4:LET ARR(6)=1:LET ARR(7)=5:LET ARR(8)=9
120 PRINT AM(ARR)
130 DEF AM(REF A)
140   LET T=0
150   FOR I=LBOUND(A) TO UBOUND(A)
160     LET T=T+A(I)
170   NEXT
180   LET AM=T/SIZE(A)
190 END DEF```

## bc

Uses the current scale for calculating the mean.

```define m(a[], n) {
auto i, s

for (i = 0; i < n; i++) {
s += a[i]
}
return(s / n)
}
```

## Befunge

The first input is the length of the vector. If a length of 0 is entered, the result is equal to `0/0`.

```&:0\:!v!:-1<
@./\\$_\&+\^
```

## blz

```:mean(vec)
vec.fold_left(0, (x, y -> x + y)) / vec.length()
end```

## Bracmat

Here are two solutions. The first uses a while loop, the second scans the input by backtracking.

```(mean1=
sum length n
.   0:?sum:?length
&   whl
' ( !arg:%?n ?arg
& 1+!length:?length
& !n+!sum:?sum
)
& !sum*!length^-1
);

(mean2=
sum length n
.     0:?sum:?length
&   !arg
:   ?
( #%@?n
& 1+!length:?length
& !n+!sum:?sum
& ~
)
?
| !sum*!length^-1
);```

To test with a list of all numbers 1 .. 999999:

```( :?test
& 1000000:?Length
& whl'(!Length+-1:?Length:>0&!Length !test:?test)
& out\$mean1\$!test
& out\$mean2\$!test
)```

## Brat

```mean = { list |
true? list.empty?, 0, { list.reduce(0, :+) / list.length }
}

p mean 1.to 10  #Prints 5.5```

## Burlesque

```blsq ) {1 2 2.718 3 3.142}av
2.372
blsq ) {}av
NaN```

## BQN

Defines a tacit Avg function which works on any simple numeric list.

```Avg ← +´÷≠

Avg 1‿2‿3‿4
```
```2.5
```

## C

Compute mean of a `double` array of given length. If length is zero, does whatever `0.0/0` does (usually means returning `NaN`).

```#include <stdio.h>

double mean(double *v, int len)
{
double sum = 0;
int i;
for (i = 0; i < len; i++)
sum += v[i];
return sum / len;
}

int main(void)
{
double v[] = {1, 2, 2.718, 3, 3.142};
int i, len;
for (len = 5; len >= 0; len--) {
printf("mean[");
for (i = 0; i < len; i++)
printf(i ? ", %g" : "%g", v[i]);
printf("] = %g\n", mean(v, len));
}

return 0;
}
```
Output:
```
mean[1, 2, 2.718, 3, 3.142] = 2.372
mean[1, 2, 2.718, 3] = 2.1795
mean[1, 2, 2.718] = 1.906
mean[1, 2] = 1.5
mean = 1
mean[] = -nan

```

## C#

```using System;
using System.Linq;

class Program
{
static void Main()
{
Console.WriteLine(new[] { 1, 2, 3 }.Average());
}
}
```

Alternative version (not using the built-in function):

```using System;

class Program
{
static void Main(string[] args)
{
double average = 0;

double[] numArray = { 1, 2, 3, 4, 5 };
average = Average(numArray);

Console.WriteLine(average); // Output is 3

// Alternative use
average = Average(1, 2, 3, 4, 5);

Console.WriteLine(average); // Output is still 3
}

static double Average(params double[] nums)
{
double d = 0;

foreach (double num in nums)
d += num;
return d / nums.Length;
}
}
```

## C++

Library: STL
```#include <vector>

double mean(const std::vector<double>& numbers)
{
if (numbers.size() == 0)
return 0;

double sum = 0;
for (std::vector<double>::iterator i = numbers.begin(); i != numbers.end(); i++)
sum += *i;
return sum / numbers.size();
}
```

Shorter (and more idiomatic) version:

```#include <vector>
#include <algorithm>

double mean(const std::vector<double>& numbers)
{
if (numbers.empty())
return 0;
return std::accumulate(numbers.begin(), numbers.end(), 0.0) / numbers.size();
}
```

Idiomatic version templated on any kind of iterator:

```#include <iterator>
#include <algorithm>

template <typename Iterator>
double mean(Iterator begin, Iterator end)
{
if (begin == end)
return 0;
return std::accumulate(begin, end, 0.0) / std::distance(begin, end);
}
```

## Chef

```Mean.

Chef has no way to detect EOF, so rather than interpreting
some arbitrary number as meaning "end of input", this program
expects the first input to be the sample size. Pass in the samples
themselves as the other inputs. For example, if you wanted to
compute the mean of 10, 100, 47, you could pass in 3, 10, 100, and
47. To test the "zero-length vector" case, you need to pass in 0.

Ingredients.
0 g Sample Size
0 g Counter
0 g Current Sample

Method.
Take Sample Size from refrigerator.
Put Sample Size into mixing bowl.
Fold Counter into mixing bowl.
Put Current Sample into mixing bowl.
Loop Counter.
Take Current Sample from refrigerator.
Add Current Sample into mixing bowl.
Endloop Counter until looped.
If Sample Size.
Divide Sample Size into mixing bowl.
Put Counter into 2nd mixing bowl.
Fold Sample Size into 2nd mixing bowl.
Endif until ifed.
Pour contents of mixing bowl into baking dish.

Serves 1.```

## Clojure

Returns a ratio:

```(defn mean [sq]
(if (empty? sq)
0
(/ (reduce + sq) (count sq))))
```

Returns a float:

```(defn mean [sq]
(if (empty? sq)
0
(float (/ (reduce + sq) (count sq)))))
```

## COBOL

Intrinsic function:

```FUNCTION MEAN(some-table (ALL))
```

Sample implementation:

```       IDENTIFICATION DIVISION.
PROGRAM-ID. find-mean.

DATA DIVISION.
LOCAL-STORAGE SECTION.
01  i                       PIC 9(4).

01  summ                    USAGE FLOAT-LONG.

01  nums-area.
03  nums-len            PIC 9(4).
03  nums                USAGE FLOAT-LONG
OCCURS 0 TO 1000 TIMES
DEPENDING ON nums-len.

01  result                  USAGE FLOAT-LONG.

PROCEDURE DIVISION USING nums-area, result.
IF nums-len = 0
MOVE 0 TO result
GOBACK
END-IF

DIVIDE FUNCTION SUM(nums (ALL)) BY nums-len GIVING result

GOBACK
.
```

## Cobra

```class Rosetta
def mean(ns as List<of number>) as number
if ns.count == 0
return 0
else
sum = 0.0
for n in ns
sum += n
return sum / ns.count

def main
print "mean of [[]] is [.mean(List<of number>())]"
print "mean of [[1,2,3,4]] is [.mean([1.0,2.0,3.0,4.0])]"```

Output:

```mean of [] is 0
mean of [1, 2, 3, 4] is 2.5
```

## CoffeeScript

```mean = (array) ->
return 0 if array.length is 0
sum = array.reduce (s,i,0) -> s += i
sum / array.length

```

## Common Lisp

With Reduce

```(defun mean (&rest sequence)
(when sequence
(/ (reduce #'+ sequence) (length sequence))))
```

With Loop

```(defun mean (list)
(when list
(/ (loop for i in list sum i)
(length list))))
```

## Craft Basic

```dim a[3, 1, 4, 1, 5, 9]

arraysize s, a

for i = 0 to s - 1

let t = t + a[i]

next i

print t / s
```
Output:
`3.83`

## Crystal

```# Crystal will return NaN if an empty array is passed
def mean(arr) : Float64
arr.sum / arr.size.to_f
end
```

## D

### Imperative Version

```real mean(Range)(Range r) pure nothrow @nogc {
real sum = 0.0;
int count;

foreach (item; r) {
sum += item;
count++;
}

if (count == 0)
return 0.0;
else
return sum / count;
}

void main() {
import std.stdio;

int[] data;
writeln("Mean: ", data.mean);
data = [3, 1, 4, 1, 5, 9];
writeln("Mean: ", data.mean);
}
```
Output:
```mean: 0
mean: 3.83333```

### More Functional Version

```import std.stdio, std.algorithm, std.range;

real mean(Range)(Range r) pure nothrow @nogc {
return r.sum / max(1.0L, r.count);
}

void main() {
writeln("Mean: ", (int[]).init.mean);
writeln("Mean: ", [3, 1, 4, 1, 5, 9].mean);
}
```
Output:
```Mean: 0
Mean: 3.83333```

### More Precise Version

A (naive?) version that tries to minimize precision loss (but already the sum algorithm applied to a random access range of floating point values uses a more precise summing strategy):

```import std.stdio, std.conv, std.algorithm, std.math, std.traits;

CommonType!(T, real) mean(T)(T[] n ...) if (isNumeric!T) {
alias E = CommonType!(T, real);
auto num = n.dup;
num.schwartzSort!(abs, "a > b");
return num.map!(to!E).sum(0.0L) / max(1, num.length);
}

void main() {
writefln("%8.5f", mean((int[]).init));
writefln("%8.5f", mean(     0, 3, 1, 4, 1, 5, 9, 0));
writefln("%8.5f", mean([-1e20, 3, 1, 4, 1, 5, 9, 1e20]));
}
```
Output:
``` 0.00000
2.87500
2.87500```

## Dart

```num mean(List<num> l) => l.reduce((num p, num n) => p + n) / l.length;

void main(){
print(mean([1,2,3,4,5,6,7]));
}
```
Output:
`4.0`

## dc

This is not a translation of the bc solution. Array handling would add some complexity. This one-liner is similar to the K solution.

```1 2 3 5 7 zsn1k[+z1<+]ds+xln/p
3.6```

An expanded example, identifying an empty sample set, could be created as a file, e.g., amean.cd:

```[[Nada Mean: ]Ppq]sq
zd0=qsn [stack length = n]sz
1k [precision can be altered]sz
[+z1<+]ds+x[Sum: ]Pp
ln/[Mean: ]Pp
[Sample size: ]Plnp```

By saving the sample set "1 2 3 5 7" in a file (sample.dc), the routine, listing summary information, could be called in a command line:

```\$ dc sample.dc amean.cd
Sum: 18
Mean: 3.6
Sample size: 5
\$```

## Delphi

```program AveragesArithmeticMean;

{\$APPTYPE CONSOLE}

uses Types;

function ArithmeticMean(aArray: TDoubleDynArray): Double;
var
lValue: Double;
begin
Result := 0;

for lValue in aArray do
Result := Result + lValue;
if Result > 0 then
Result := Result / Length(aArray);
end;

begin
Writeln(Mean(TDoubleDynArray.Create()));
Writeln(Mean(TDoubleDynArray.Create(1,2,3,4,5)));
end.
```

## Dyalect

```func avg(args...) {
var acc = .0
var len = 0
for x in args {
len += 1
acc += x
}
acc / len
}

avg(1, 2, 3, 4, 5, 6)```

## E

Slightly generalized to support any object that allows iteration.

```def meanOrZero(numbers) {
var count := 0
var sum := 0
for x in numbers {
sum += x
count += 1
}
return sum / 1.max(count)
}```

## EasyLang

```proc mean . f[] r .
for i = 1 to len f[]
s += f[i]
.
r = s / len f[]
.
f[] = [ 1 2 3 4 5 6 7 8 ]
mean f[] r
print r
```

## EchoLisp

(mean values) is included in math.lib. values may be a list, vector, sequence, or any kind of procrastinator.

```(lib 'math)
(mean '(1 2 3 4)) ;; mean of a list
→ 2.5
(mean #(1 2 3 4)) ;; mean of a vector
→ 2.5

(lib 'sequences)
(mean [1 3 .. 10]) ;; mean of a sequence
→ 5

;; error handling
(mean 'elvis)
⛔ error: mean : expected sequence : elvis
(mean ())
💣 error: mean : null is not an object
(mean #())
😐 warning: mean : zero-divide : empty-vector
→ 0
(mean [2 2 .. 2])
😁 warning: mean : zero-divide : empty-sequence
→ 0
```

## ECL

```AveVal(SET OF INTEGER s) := AVE(s);

//example usage

SetVals := [14,9,16,20,91];
AveVal(SetVals) //returns 30.0 ;
```

## EDSAC order code

Extends the RC task by finding the arithmetic mean for each of several data sets. Each data set is preceded by the number of data. A count of 0 is not an error but signals that there are no more data sets.

The program needs to avoid the possibility of arithmetic overflow, as pointed out in the F# solution. The moving average used there is not well-suited to EDSAC, on which division had to be done by calling a subroutine. After reading the number of data N, and leaving the trivial case N = 1 for separate treatment, the program first calculates 1/N, then multiplies each value by 1/N before adding it into the result.

```[Averages/Arithmetic mean - Rosetta Code]

[EDSAC program (Initial Orders 2) to find and print the average of
a sequence of 35-bit fractional values.
Values are read from tape, preceded by an integer count.]

[Library subroutine M3, runs at load time and is then overwritten.
Prints header; here, last character sets teleprinter to figures.]
PF GK IF AF RD LF UF OF E@ A6F G@ E8F EZ PF
*!!!!!COUNT!!!!!!AVERAGE@&#..   [PZ]

[Main routine: must be at even address]
T214K GK
   PF PF         [average value]
   PF PF         [reciprocal of data count]
   PF            [data count]
   PD            [17-bit constant 1; also serves as '0' for printing]
   @F            [carriage return]
   &F            [line feed]
   !F            [space]
   MF            [dot (in figures mode)]
   K4096F        [teleprinter null]
[Entry and outer loop]
   A11@
G56F          [call library subroutine R4, sets 0D := data count N]
SD E64@       [exit if N = 0]
T4F           [clear acc]
AF T4@        [load and save N (assumed < 2^16)]
   A18@ G156F    [print N (clears acc)]
TD            [clear whole of 0D, including sandwich bit]
T4D           [same for 4D]
A4@ S2F       [acc := N - 2]
A2F           [restore N after test]
T5F           [store N in 4D high word]
A5@ T1F       [store 1 in 0D high word]
   A29@ G120F    [call library subroutine D6, sets 0D := 0D/4D]
T#@           [clear average]
[Inner loop]
   T4@           [update negative loop counter]
   A36@ G78F     [read next datum to 0D (clears acc)]
H2#@          [mult reg := 1/N]
VD            [acc := datum/N]
A4@ A5@       [increment negative loop counter]
G35@          [loop until counter = 0]
   O8@ O8@       [print 2 spaces]
[Print the average value.
NB: Library subroutine P1 requires non-negative input and prints only the
digits after the decimal point. Formatting has to be done by the caller.]
   A#@           [load average (order also serves as minus sign)]
G52@          [jump if average < 0]
TD            [pass average to subroutine P1]
O65@          [print plus sign (or could be space)]
E56@          [join common code]
   TD            [average < 0; clear acc]
S#@ TD        [pass abs(average) to subroutine P1]
O47@          [print minus sign]
   O5@ O9@       [common code: print '0.']
   A58@ G192F    [call P1 to print abs(average)]
P8F           [8 decimal places]
O6@ O7@       [print CR, LF]
E11@          [loop back always (because acc = 0)]
[Jump to here if data count = 0, means end of data]
   O10@          [print null to flush teleprinter buffer]
   ZF            [halt the machine (order also serves as plus sign)]
   TF            [clear acc]
   A67@ G78F     [read datum to 0D]

[The following puts the entry address into location 50,
so that it can be accessed via the X parameter (see end of program).
This is done in case the data is input from a separate tape.]
T50K P11@ T11Z

[Library subroutine R4.
Input of one signed integer, returned in 0D.]
T56K
GKA3FT21@T4DH6@E11@P5DJFT6FVDL4FA4DTDI4FA4FS5@G7@S5@G20@SDTDT6FEF

[Library subroutine R3.
Input of one long signed decimal fraction, returned in 0D.]
T78K
H2#HN4DLDYFTDT28#ZPFT27ZTFP610D@524DP5DPDIFS4HG37@S4DT4DT7FA1HT9@E18@

[Library subroutine D6 - Division, accurate, fast.
36 locations, workspace 6D and 8D.
0D := 0D/4D, where 4D <> 0, -1.]
T120K
T6DE25@U8DN8DA6DT6DH6DS6DN4DA4DYFG21@SDVDTDEFW1526D

[Library subroutine P7: print strictly positive integer in 0D.]
T156K
GKA3FT26@H28#@NDYFLDT4DS27@TFH8@S8@T1FV4DAFG31@SFLDUFOFFFSF
L4FT4DA1FA27@G11@T28#ZPFT27ZP1024FP610D@524D!FO30@SFL8FE22@

[Library subroutine P1: print non-negative fraction in 0D, without '0.']
T192K
GKA18@U17@S20@T5@H19@PFT5@VDUFOFFFSFL4FTDA5@A2FG6@EFU3FJFM1F

[==========================================================================
On the original EDSAC, the following (without the whitespace and comments)
might have been input on a separate tape.]

E25K TX GK
EZ            [define entry point]
PF            [acc = 0 on entry]

[Counts and data values to be read by library subroutines R3 and R4 respectively.
Note (1) Sign comes *after* value (2) In the data, leading '0.' is omitted.]
7+ 1-2-3-4-5+2-3-
1+ 987654321+
9+ 01+04+09+16+25+36+49+64+81+
9+ 01-04+09-16+25-36+49-64+81-
[Daily minimum temperature (unit = 10 deg. C), Cambridge, UK, January 2000]
31+ 34+14+49+00+04+48+05+48+23-35-07-75+19+03+
26+27+17-06-52+22-17+18+15+03-33-11-04-01-44+89+95+
0+```
Output:
```     COUNT      AVERAGE
7  -0.14285714
1  +0.98765432
9  +0.31666666
9  -0.05000000
31  +0.16774193
```

## Elena

ELENA 6.x:

```import extensions;

extension op
{
average()
{
real sum := 0;
int count := 0;

var enumerator := self.enumerator();

while (enumerator.next())
{
sum += *enumerator;
count += 1;
};

^ sum / count
}
}

public program()
{
var array := new int[]{1, 2, 3, 4, 5, 6, 7, 8};
console.printLine(
"Arithmetic mean of {",array.asEnumerable(),"} is ",
}```
Output:
```Arithmetic mean of {1,2,3,4,5,6,7,8} is 4.5
```

## Elixir

```defmodule Average do
def mean(list), do: Enum.sum(list) / length(list)
end
```

## Emacs Lisp

```(defun mean (lst)
(/ (float (apply '+ lst)) (length lst)))
(mean '(1 2 3 4))
```
Library: Calc
```(let ((x '(1 2 3 4)))
(calc-eval "vmean(\$1)" nil (append '(vec) x)))
```

## EMal

```fun mean = real by some real values
real sum
int count
for each real value in values
sum += value
++count
end
return when(count == 0, 0.0, sum / count)
end
writeLine(mean())
writeLine(mean(3,1,4,1,5,9))```
Output:
```0.0
3.8333333333333333333333333333
```

## Erlang

```mean([]) -> 0;
mean(L)  -> lists:sum(L)/erlang:length(L).
```

## Euphoria

```function mean(sequence s)
atom sum
if length(s) = 0 then
return 0
else
sum = 0
for i = 1 to length(s) do
sum += s[i]
end for
return sum/length(s)
end if
end function

sequence test
test = {1.0, 2.0, 5.0, -5.0, 9.5, 3.14159}
? mean(test)```

## Excel

Assuming the values are entered in the A column, type into any cell which will not be part of the list:

`=AVERAGE(A1:A10)`

Assuming 10 values will be entered, alternatively, you can just type:

`=AVERAGE(`

and then select the start and end cells, not necessarily in the same row or column.

The output for the first expression, for the set {x | 1 <= x <= 10, x E N} is

```1	5,5
2
3
4
5
6
7
8
9
10
```

## F#

The following computes the running mean using a tail-recursive approach. If we just sum all the values then divide by the number of values then we will suffer from overflow problems for large lists. See wikipedia about the moving average computation.

```let avg (a:float) (v:float) n =
a + (1. / ((float n) + 1.)) * (v - a)

let mean_series list =
let a, _ = List.fold_left (fun (a, n) h -> avg a (float h) n, n + 1) (0., 0) list in
a
```

Checking this:

``` > mean_series [1; 8; 2; 8; 1; 7; 1; 8; 2; 7; 3; 6; 1; 8; 100] ;;
val it : float = 10.86666667
> mean_series [] ;;
val it : float = 0.0
```

We can also make do with the built-in List.average function:

```List.average [4;1;7;5;8;4;5;2;1;5;2;5]
```

## Factor

```USING: math math.statistics ;

: arithmetic-mean ( seq -- n )
[ 0 ] [ mean ] if-empty ;
```

Tests:

```( scratchpad ) { 2 3 5 } arithmetic-mean >float
3.333333333333333
```

## Fantom

```class Main
{
static Float average (Float[] nums)
{
if (nums.size == 0) return 0.0f
Float sum := 0f
nums.each |num| { sum += num }
return sum / nums.size.toFloat
}

public static Void main ()
{
[[,], [1f], [1f,2f,3f,4f]].each |Float[] i|
{
echo ("Average of \$i is: " + average(i))
}
}
}```

## Fish

```!vl0=?vl1=?vl&!
v<  +<>0n; >n;
>l1)?^&,n;
```

Must be called with the values pre-populated on the stack, which can be done in the fish.py interpreter with the -v switch:

`fish.py mean.fish -v 10 100 47 207.4`

which generates:

`91.1`

## Forth

```: fmean ( addr n -- f )
0e
dup 0= if 2drop exit then
tuck floats bounds do
i f@ f+
1 floats +loop
0 d>f f/ ;

create test 3e f, 1e f, 4e f, 1e f, 5e f, 9e f,
test 6 fmean f.     \ 3.83333333333333
```

## Fortran

In ISO Fortran 90 or later, use the SUM intrinsic, the SIZE intrinsic and the MAX intrinsic (to avoid divide by zero):

```real, target, dimension(100) :: a = (/ (i, i=1, 100) /)
real, dimension(5,20) :: b = reshape( a, (/ 5,20 /) )
real, pointer, dimension(:) :: p => a(2:1)       ! pointer to zero-length array
real :: mean, zmean, bmean
real, dimension(20) :: colmeans
real, dimension(5) :: rowmeans

mean = sum(a)/size(a)                ! SUM of A's elements divided by SIZE of A
mean = sum(a)/max(size(a),1)         ! Same result, but safer code
! MAX of SIZE and 1 prevents divide by zero if SIZE == 0 (zero-length array)

zmean = sum(p)/max(size(p),1)        ! Here the safety check pays off. Since P is a zero-length array,
! expression becomes "0 / MAX( 0, 1 ) -> 0 / 1 -> 0", rather than "0 / 0 -> NaN"

bmean = sum(b)/max(size(b),1)        ! multidimensional SUM over multidimensional SIZE

rowmeans = sum(b,1)/max(size(b,2),1) ! SUM elements in each row (dimension 1)
! dividing by the length of the row, which is the number of columns (SIZE of dimension 2)
colmeans = sum(b,2)/max(size(b,1),1) ! SUM elements in each column (dimension 2)
! dividing by the length of the column, which is the number of rows (SIZE of dimension 1)
```

## FreeBASIC

```' FB 1.05.0 Win64

Function Mean(array() As Double) As Double
Dim length As Integer = Ubound(array) - Lbound(array) + 1
If length = 0 Then
Return 0.0/0.0 'NaN
End If
Dim As Double sum = 0.0
For i As Integer = LBound(array) To UBound(array)
sum += array(i)
Next
Return sum/length
End Function

Function IsNaN(number As Double) As Boolean
Return Str(number) = "-1.#IND" ' NaN as a string in FB
End Function

Dim As Integer n, i
Dim As Double num
Print "Sample input and output"
Print
Do
Input "How many numbers are to be input ? : ", n
Loop Until n > 0
Dim vector(1 To N) As Double
Print
For i = 1 to n
Print "  Number #"; i; " : ";
Input "", vector(i)
Next
Print
Print "Mean is"; Mean(vector())
Print
Erase vector
num = Mean(vector())
If IsNaN(num) Then
Print "After clearing the vector, the mean is 'NaN'"
End If
Print
Print "Press any key to quit the program"
Sleep```
Output:
```Sample input and output

How many numbers are to be input ? : 6

Number # 1 : 12
Number # 2 : 18
Number # 3 : 5.6
Number # 4 : 6
Number # 5 : 23
Number # 6 : 17

Mean is 13.6

After clearing the vector, the mean is 'NaN'
```

## Frink

The following works on arrays or sets. If the collection is empty, this returns the special value `undef`.

`mean[x] := length[x] > 0 ? sum[x] / length[x] : undef`

## FutureBasic

```local fn MeanAverageOfNumberArray( numberArr as CFArrayRef ) as CFStringRef
CFStringRef result = NULL
if len(numberArr) == 0 then result = @"Mean undefined for empty array." : exit fn
result = fn StringWithFormat( @"Mean average of %d numbers: %@", len(numberArr), fn ObjectValueForKeyPath( numberArr, @"@avg.self" ) )
end fn = result

CFArrayRef numberArray
numberArray = @[@1, @2, @3, @4, @5, @6, @7, @8, @9, @10]
print fn MeanAverageOfNumberArray( numberArray )
numberArray = @[@3, @1, @4, @1, @5, @9]
print fn MeanAverageOfNumberArray( numberArray )

HandleEvents```
Output:
```Mean average of 10 numbers: 5.5
Man average of 6 numbers: 3.83333333333333333333333333333333333333
```

## GAP

```Mean := function(v)
local n;
n := Length(v);
if n = 0 then
return 0;
else
return Sum(v)/n;
fi;
end;

Mean([3, 1, 4, 1, 5, 9]);
# 23/6
```

## GEORGE

```R (n) P ;
0
1, n rep (i)
R P +
]
n div
P```

Output:

``` 7.000000000000000
1.500000000000000E+0001
1.300000000000000E+0001
8.000000000000000
2.500000000000000E+0001
7.400000000000000E+0001
3.100000000000000E+0001
2.900000000000000E+0001
1.700000000000000E+0001
4.300000000000000E+0001
2.620000000000000E+0001
```

## GFA Basic

This works for arrays of integers.

```DIM a%(10)
FOR i%=0 TO 10
a%(i%)=i%*2
PRINT "element ";i%;" is ";a%(i%)
NEXT i%
PRINT "mean is ";@mean(a%)
'
FUNCTION mean(a%)
LOCAL i%,size%,sum
' find size of array,
size%=DIM?(a%())
' return 0 for empty arrays
IF size%<=0
RETURN 0
ENDIF
' find sum of all elements
sum=0
FOR i%=0 TO size%-1
sum=sum+a%(i%)
NEXT i%
' mean is sum over size
RETURN sum/size%
ENDFUNC
```

## Go

A little more elaborate that the task requires. The function "mean" fulfills the task of "a program to find the mean." As a Go idiom, it returns an ok value of true if result m is valid. An ok value of false means the input "vector" (a Go slice) was empty. The fancy accuracy preserving algorithm is a little more than was called more. The program main is a test program demonstrating the ok idiom and several data cases.

```package main

import (
"fmt"
"math"
)

func mean(v []float64) (m float64, ok bool) {
if len(v) == 0 {
return
}
// an algorithm that attempts to retain accuracy
// with widely different values.
var parts []float64
for _, x := range v {
var i int
for _, p := range parts {
sum := p + x
var err float64
switch ax, ap := math.Abs(x), math.Abs(p); {
case ax < ap:
err = x - (sum - p)
case ap < ax:
err = p - (sum - x)
}
if err != 0 {
parts[i] = err
i++
}
x = sum
}
parts = append(parts[:i], x)
}
var sum float64
for _, x := range parts {
sum += x
}
return sum / float64(len(v)), true
}

func main() {
for _, v := range [][]float64{
[]float64{},                         // mean returns ok = false
[]float64{math.Inf(1), math.Inf(1)}, // answer is +Inf

// answer is NaN, and mean returns ok = true, indicating NaN
// is the correct result
[]float64{math.Inf(1), math.Inf(-1)},

[]float64{3, 1, 4, 1, 5, 9},

// large magnitude numbers cancel. answer is mean of small numbers.
[]float64{1e20, 3, 1, 4, 1, 5, 9, -1e20},

[]float64{10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, 0, 0, 0, .11},
[]float64{10, 20, 30, 40, 50, -100, 4.7, -11e2},
} {
fmt.Println("Vector:", v)
if m, ok := mean(v); ok {
fmt.Printf("Mean of %d numbers is %g\n\n", len(v), m)
} else {
fmt.Println("Mean undefined\n")
}
}
}
```
Output:
```Vector: []
Mean undefined

Vector: [+Inf +Inf]
Mean of 2 numbers is +Inf

Vector: [+Inf -Inf]
Mean of 2 numbers is NaN

Vector: [3 1 4 1 5 9]
Mean of 6 numbers is 3.8333333333333335

Vector: [1e+20 3 1 4 1 5 9 -1e+20]
Mean of 8 numbers is 2.875

Vector: [10 9 8 7 6 5 4 3 2 1 0 0 0 0 0.11]
Mean of 15 numbers is 3.674

Vector: [10 20 30 40 50 -100 4.7 -1100]
Mean of 8 numbers is -130.6625
```

## Groovy

```def avg = { list -> list == [] ? 0 : list.sum() / list.size() }
```

Test Program:

```println avg(0..9)
println avg([2,2,2,4,2])
println avg ([])
```

Output:

```4.5
2.4
0```

This function works if the element type is an instance of Fractional:

```mean :: (Fractional a) => [a] -> a
mean [] = 0
mean xs = sum xs / Data.List.genericLength xs
```

But some types, e.g. integers, are not Fractional; the following function works for all Real types:

```meanReals :: (Real a, Fractional b) => [a] -> b
meanReals = mean . map realToFrac
```

If you want to avoid keeping the list in memory and traversing it twice:

```{-# LANGUAGE BangPatterns #-}

import Data.List (foldl') --'

mean
:: (Real n, Fractional m)
=> [n] -> m
mean xs =
let (s, l) =
foldl' --'
f
(0, 0)
xs
in realToFrac s / l
where
f (!s, !l) x = (s + x, l + 1)

main :: IO ()
main = print \$ mean [1 .. 100]
```

## HicEst

```REAL :: vec(100)               ! no zero-length arrays in HicEst

vec = \$ - 1/2               ! 0.5 ... 99.5
mean = SUM(vec) / LEN(vec)  ! 50
END```

## Hy

Returns None if the input is of length zero.

```(defn arithmetic-mean [xs]
(if xs
(/ (sum xs) (len xs))))
```

## Icon and Unicon

```procedure main(args)
every (s := 0) +:= !args
write((real(s)/(0 ~= *args)) | 0)
end
```

Sample outputs:

```->am 1 2 3 4 5 6 7
4.0
->am
0
->```

## IDL

If truly only the mean is wanted, one could use

```x = [3,1,4,1,5,9]
print,mean(x)
```

But mean() is just a thin wrapper returning the zeroth element of moment() :

```print,moment(x)
; ==>
3.83333      8.96667     0.580037     -1.25081
```

which are mean, variance, skewness and kurtosis.

There are no zero-length vectors in IDL. Every variable has at least one value or otherwise it is <Undefined>.

## J

```mean=: +/ % #
```

That is, sum divided by the number of items. The verb also works on higher-ranked arrays. For example:

```   mean 3 1 4 1 5 9
3.83333
mean \$0         NB. \$0 is a zero-length vector
0
x=: 20 4 ?@\$ 0  NB. a 20-by-4 table of random (0,1) numbers
mean x
0.58243 0.402948 0.477066 0.511155
```

The computation can also be written as a loop. It is shown here for comparison only and is highly non-preferred compared to the version above.

```mean1=: 3 : 0
z=. 0
for_i. i.#y do. z=. z+i{y end.
z % #y
)
mean1 3 1 4 1 5 9
3.83333
mean1 \$0
0
mean1 x
0.58243 0.402948 0.477066 0.511155
```

## Java

Works with: Java version 1.5+
```public static double avg(double... arr) {
double sum = 0.0;
for (double x : arr) {
sum += x;
}
return sum / arr.length;
}```

## JavaScript

### ES5

```function mean(array)
{
var sum = 0, i;
for (i = 0; i < array.length; i++)
{
sum += array[i];
}
return array.length ? sum / array.length : 0;
}

alert( mean( [1,2,3,4,5] ) );   // 3
alert( mean( [] ) );            // 0
```

Using the native function `.forEach()`:

```function mean(array) {
var sum = 0;
array.forEach(function(value){
sum += value;
});
return array.length ? sum / array.length : 0;
}

alert( mean( [1,2,3,4,5] ) );   // 3
```

Using the native function `.reduce()`:

```function mean(array) {
return !array.length ? 0
: array.reduce(function(pre, cur, i) {
return (pre * i + cur) / (i + 1);
});
}

alert( mean( [1,2,3,4,5] ) );   // 3
alert( mean( [] ) );            // 0
```

Extending the `Array` prototype:

```Array.prototype.mean = function() {
return !this.length ? 0
: this.reduce(function(pre, cur, i) {
return (pre * i + cur) / (i + 1);
});
}

```

Library: Functional
```function mean(a)
{
return a.length ? Functional.reduce('+', 0, a) / a.length : 0;
}
```

### ES6

```(sample => {

// mean :: [Num] => (Num | NaN)
let mean = lst => {
let lng = lst.length;

return lng ? (
lst.reduce((a, b) => a + b, 0) / lng
) : NaN;
};

return mean(sample);

})([1, 2, 3, 4, 5, 6, 7, 8, 9]);
```
Output:
```5
```

## Joy

`DEFINE avg == dup 0. [+] fold swap size 1 max /.`

## jq

The mean of an array of numbers can be computed by simply writing

`add/length`

This definition raises an error condition if the array is empty, so it may make sense to define mean as follows, null being jq's null value:

```def mean: if length == 0 then null
end;```

## Julia

Julia's built-in mean function accepts AbstractArrays (vector, matrix, etc.)

```julia> using Statistics; mean([1,2,3])
2.0
julia> mean(1:10)
5.5
julia> mean([])
ERROR: mean of empty collection undefined: []
```

## K

```  mean: {(+/x)%#x}
mean 1 2 3 5 7
3.6
mean@!0    / empty array
0.0
```

## Kotlin

Kotlin has builtin functions for some collection types. Example:

```fun main(args: Array<String>) {
val nums = doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0)
println("average = %f".format(nums.average()))
}
```

## KQL

```let dataset = datatable(values:real)[
1,      1.5,    3,  5,      6.5];

dataset|summarize avg(values)```

Output:

```
avg_values
3.4
```

## LabVIEW

This image is a VI Snippet, an executable image of LabVIEW code. The LabVIEW version is shown on the top-right hand corner. You can download it, then drag-and-drop it onto the LabVIEW block diagram from a file browser, and it will appear as runnable, editable code. ## Lambdatalk

```{def mean
{lambda {:s}
{if {S.empty? :s}
then 0
else {/ {+ :s} {S.length :s}}}}}

{mean {S.serie 0 1000}}
-> 500
```

## langur

The built-in mean() function works with an array, hash, or range of numbers.

We could use fold() to write a function that takes an array and calculates the mean.

Works with: langur version 0.6.6
```val .mean = f(.x) fold(f{+}, .x) / len(.x)

writeln "  custom: ", .mean([7, 3, 12])
writeln "built-in: ", mean([7, 3, 12])```
Output:
```  custom: 7.333333333333333333333333333333333
built-in: 7.333333333333333333333333333333333```

## Lasso

```define average(a::array) => {
not #a->size ? return 0
local(x = 0.0)
with i in #a do => { #x += #i }
return #x / #a->size
}

average(array(1,2,5,17,7.4)) //6.48
```

## LFE

### 1-Arity

```(defun mean (data)
(/ (lists:sum data)
(length data)))
```

Usage:

```> (mean '(1 1))
1.0
> (mean '(1 2))
1.5
> (mean '(2 10))
6.0
> (mean '(6 12 18 24 30 36 42 48 54 60 66 72 78))
42.0
```

### n-Arity

Functions in LFE (and Erlang) have set arity, but macros can be used to provide the same use as n-arity functions:

```(defmacro mean args
`(/ (lists:sum ,args)
,(length args)))
```

Usage:

```> (mean 42)
42.0
> (mean 18 66)
42.0
> (mean 6 12 18 24 30 36 42 48 54 60 66 72 78)
42.0
```

## Liberty BASIC

```total=17
dim nums(total)
for i = 1 to total
nums(i)=i-1
next

for j = 1 to total
sum=sum+nums(j)
next
if total=0 then mean=0 else mean=sum/total
print "Arithmetic mean: ";mean```

## Limbo

```implement Command;

include "sys.m";
sys: Sys;

include "draw.m";

include "sh.m";

init(nil: ref Draw->Context, nil: list of string)
{

a := array[] of {1.0, 2.0, 500.0, 257.0};
sys->print("mean of a: %f\n", getmean(a));
}

getmean(a: array of real): real
{
n: real = 0.0;
for (i := 0; i < len a; i++)
n += a[i];
return n / (real len a);
}
```

## Lingo

```-- v can be (2D) point, (3D) vector or list of integers/floats
on mean (v)
case ilk(v) of
#point: cnt = 2
#vector: cnt = 3
#list: cnt = v.count
otherwise: return
end case
sum = 0
repeat with i = 1 to cnt
sum = sum + v[i]
end repeat
return float(sum)/cnt
end```
```put mean(point(1, 2.5))
-- 1.7500
put mean(vector(1.2, 4.7, 5.6))
-- 3.8333
put mean([6,12,18,24,30,36,42,48,54,60,66,72,78])
-- 42.0000```

## LiveCode

Livecode provides arithmeticMean (avg, average) built-in.

```average(1,2,3,4,5)  -- 3
average(empty)  -- 0```

## Logo

```to average :l
if empty? :l [output 0]
output quotient apply "sum :l count :l
end
print average [1 2 3 4]    ; 2.5```

## Logtalk

Logtalk's standard library provides an arithmetic average predicate but we ignore it here. Representing a vector using a list:

```:- object(averages).

:- public(arithmetic/2).

% fails for empty vectors
arithmetic([X| Xs], Mean) :-
sum_and_count([X| Xs], 0, Sum, 0, Count),
Mean is Sum / Count.

% use accumulators to make the predicate tail-recursive
sum_and_count([], Sum, Sum, Count, Count).
sum_and_count([X| Xs], Sum0, Sum, Count0, Count) :-
Sum1 is Sum0 + X,
Count1 is Count0 + 1,
sum_and_count(Xs, Sum1, Sum, Count1, Count).

:- end_object.
```

Sample output:

```| ?- averages::arithmetic([1,2,3,4,5,6,7,8,9,10], Mean).
Mean = 5.5
yes
```

## LSL

```integer MAX_ELEMENTS = 10;
integer MAX_VALUE = 100;
default {
state_entry() {
list lst = [];
integer x = 0;
for(x=0 ; x<MAX_ELEMENTS ; x++) {
lst += llFrand(MAX_VALUE);
}
llOwnerSay("lst=["+llList2CSV(lst)+"]");
llOwnerSay("Geometric Mean: "+(string)llListStatistics(LIST_STAT_GEOMETRIC_MEAN, lst));
llOwnerSay("           Max: "+(string)llListStatistics(LIST_STAT_MAX, lst));
llOwnerSay("          Mean: "+(string)llListStatistics(LIST_STAT_MEAN, lst));
llOwnerSay("        Median: "+(string)llListStatistics(LIST_STAT_MEDIAN, lst));
llOwnerSay("           Min: "+(string)llListStatistics(LIST_STAT_MIN, lst));
llOwnerSay("     Num Count: "+(string)llListStatistics(LIST_STAT_NUM_COUNT, lst));
llOwnerSay("         Range: "+(string)llListStatistics(LIST_STAT_RANGE, lst));
llOwnerSay("       Std Dev: "+(string)llListStatistics(LIST_STAT_STD_DEV, lst));
llOwnerSay("           Sum: "+(string)llListStatistics(LIST_STAT_SUM, lst));
llOwnerSay("   Sum Squares: "+(string)llListStatistics(LIST_STAT_SUM_SQUARES, lst));
}
}
```

Output:

```lst=[23.815209, 85.890704, 10.811144, 31.522696, 54.619416, 12.211729, 42.964463, 87.367889, 7.106129, 18.711078]
Geometric Mean:    27.325070
Max:    87.367889
Mean:    37.502046
Median:    27.668953
Min:     7.106129
Num Count:    10.000000
Range:    80.261761
Std Dev:    29.819840
Sum:   375.020458
Sum Squares: 22067.040048
```

## Lua

```function mean (numlist)
if type(numlist) ~= 'table' then return numlist end
num = 0
table.foreach(numlist,function(i,v) num=num+v end)
return num / #numlist
end

print (mean({3,1,4,1,5,9}))
```

## Lucid

```avg(x)
where
sum = first(x) fby sum + next(x);
n = 1 fby n + 1;
avg = sum / n;
end```

## M4

M4 handle only integers, so in order to have a slightly better math for the mean, we must pass to the mean macro integers multiplied by 100. The macro rmean could embed the macro fmean and extractdec directly, but it is a little bit clearer to keep them separated.

```define(`extractdec', `ifelse(eval(`\$1%100 < 10'),1,`0',`')eval(\$1%100)')dnl
define(`fmean', `eval(`(\$2/\$1)/100').extractdec(eval(`\$2/\$1'))')dnl
define(`mean', `rmean(`\$#', \$@)')dnl
define(`rmean', `ifelse(`\$3', `', `fmean(\$1,\$2)',dnl
`rmean(\$1, eval(\$2+\$3), shift(shift(shift(\$@))))')')dnl```
`mean(0,100,200,300,400,500,600,700,800,900,1000)`

## Maple

This version accepts any indexable structure, including numeric arrays. We use a call to the "environment variable" (dynamically scoped global) "Normalizer" to provide normalization of symbolic expressions. This can be set by the caller to adjust the strength of normalization desired.

```mean := proc( a :: indexable )
local   i;
Normalizer( add( i, i in a ) / numelems( a ) )
end proc:```

For example:

```> mean( { 1/2, 2/3, 3/4, 4/5, 5/6 } ); # set
71
---
100

> mean( [ a, 2, c, 2.3, e ] ); # list
0.8600000000 + a/5 + c/5 + e/5

> mean( Array( [ 1, sin( s ), 3, exp( I*t ), 5 ] ) ); # array
9/5 + 1/5 sin(s) + 1/5 exp(t I)

> mean( [ sin(s)^2, cos(s)^2 ] );
2             2
1/2 sin(s)  + 1/2 cos(s)

> Normalizer := simplify: # use a stronger normalizer than the default
> mean( [ sin(s)^2, cos(s)^2 ] );
1/2

> mean([]); # empty argument causes an exception to be raised.
Error, (in mean) numeric exception: division by zero```

A slightly different design computes the mean of all its arguments, instead of requiring a single container argument. This seems a little more Maple-like for a general purpose utility.

`mean := () -> Normalizer( `+`( args ) / nargs ):`

This can be called as in the following examples.

```> mean( 1, 2, 3, 4, 5 );
3

> mean( a + b, b + c, c + d, d + e, e + a );
2 a   2 b   2 c   2 d   2 e
--- + --- + --- + --- + ---
5     5     5     5     5

> mean(); # again, an exception is raised
Error, (in mean) numeric exception: division by zero```

If desired, we can add argument type-checking as follows.

`mean := ( s :: seq(algebraic) ) -> Normalizer( `+`( args ) / nargs ):`

## Mathematica / Wolfram Language

Modify the built-in Mean function to give 0 for empty vectors (lists in Mathematica):

```Unprotect[Mean];
Mean[{}] := 0
```

Examples:

```Mean[{3,4,5}]
Mean[{3.2,4.5,5.9}]
Mean[{-4, 1.233}]
Mean[{}]
Mean[{1/2,1/3,1/4,1/5}]
Mean[{a,c,Pi,-3,a}]
```

gives (a set of integers gives back an integer or a rational, a set of floats gives back a float, a set of rationals gives a rational back, a list of symbols and numbers keeps the symbols exact and a mix of exact and approximate numbers gives back an approximate number):

```4
4.53333
-1.3835
0
77/240
1/5 (-3+2 a+c+Pi)
```

## Mathprog

Summing the vector and then dividing the sum by the vector's length is slightly less boring than calling a builtin function Mean or similar.

Mathprog is never boring so this program finds a number M such that when M is subtracted from each value in the vector a second vector is formed with the property that the sum of the elements in the second vector is zero. In this case M is the Arithmetic Mean.

Euclid proved that for any vector there is only one such number and from this derived the Division Theorem.

To make it more interesting I find the Arithmectic Mean of more than a million Integers.

```/*Arithmetic Mean of a large number of Integers
- or - solve a very large constraint matrix
over 1 million rows and columns
Nigel_Galloway
March 18th., 2008.
*/

param e := 20;
set Sample := {1..2**e-1};

var Mean;
var E{z in Sample};

/* sum of variances is zero */
zumVariance: sum{z in Sample} E[z] = 0;

/* Mean + variance[n] = Sample[n] */
variances{z in Sample}: Mean + E[z] = z;

solve;

printf "The arithmetic mean of the integers from 1 to %d is %f\n", 2**e-1, Mean;

end;
```

When run this produces:

```GLPSOL: GLPK LP/MIP Solver, v4.47
Parameter(s) specified in the command line:
--nopresol --math AM.mprog
Generating zumVariance...
Generating variances...
Model has been successfully generated
Scaling...
A: min|aij| = 1.000e+000  max|aij| = 1.000e+000  ratio = 1.000e+000
Problem data seem to be well scaled
Constructing initial basis...
Size of triangular part = 1048575
GLPK Simplex Optimizer, v4.47
1048576 rows, 1048576 columns, 3145725 non-zeros
0: obj =  0.000000000e+000  infeas = 5.498e+011 (1)
*     1: obj =  0.000000000e+000  infeas = 0.000e+000 (0)
OPTIMAL SOLUTION FOUND
Time used:   2.0 secs
Memory used: 1393.8 Mb (1461484590 bytes)
The arithmetic mean of the integers from 1 to 1048575 is 524288.000000
Model has been successfully processed
```

## MATLAB

```function meanValue = findmean(setOfValues)
meanValue = mean(setOfValues);
end
```

## Maxima

```load("descriptive");
mean([2, 7, 11, 17]);
```

## MAXScript

```fn mean data =
(
total = 0
for i in data do
(
total += i
)
if data.count == 0 then 0 else total as float/data.count
)

print (mean #(3, 1, 4, 1, 5, 9))```

## Mercury

```:- module arithmetic_mean.
:- interface.

:- import_module io.

:- pred main(io::di, io::uo) is det.

:- implementation.

:- import_module float, list, require.

main(!IO) :-
io.print_line(mean([1.0, 2.0, 3.0, 4.0, 5.0]), !IO).

:- func mean(list(float)) = float.

mean([]) = func_error("mean: emtpy list").
mean(Ns @ [_ | _]) = foldl((+), Ns, 0.0) / float(length(Ns)).

:- end_module arithmetic_mean.```

Alternatively, we could use inst subtyping to ensure we get a compilation error if the mean function is called with an empty list.

```:- func mean(list(float)::in(non_empty_list)) = (float::out).

mean(Ns) = foldl((+), Ns, 0.0) / float(length(Ns)).```

## min

Returns `nan` for an empty quotation.

Works with: min version 0.37.0
`(2 3 5) avg puts!`
Output:
`3.333333333333333`

## MiniScript

```arr = [ 1, 3, 7, 8, 9, 1 ]

avg = function(arr)
avgNum = 0
for num in arr
avgNum = avgNum + num
end for
return avgNum / arr.len
end function

print avg(arr)
```

## МК-61/52

```0	П0	П1	С/П	ИП0	ИП1	*	+	ИП1	1
+	П1	/	П0	БП	03
```

Instruction: В/О С/П Number С/П Number ...

Each time you press the С/П on the indicator would mean already entered numbers.

## Modula-2

```PROCEDURE  Avg;

VAR     avg             : REAL;

BEGIN
avg := sx / n;
InOut.WriteString ("Average = ");
InOut.WriteReal (avg, 8, 2);
InOut.WriteLn
END Avg;
```

OR

```PROCEDURE Average (Data  : ARRAY OF REAL;   Samples : CARDINAL) : REAL;

(*  Calculate the average over 'Samples' values, stored in array 'Data'.     *)

VAR     sum         : REAL;
n           : CARDINAL;

BEGIN
sum := 0.0;
FOR n := 0 TO Samples - 1 DO
sum := sum + Data [n]
END;
RETURN sum / FLOAT(Samples)
END Average;
```

## MUMPS

```MEAN(X)
;X is assumed to be a list of numbers separated by "^"
QUIT:'\$DATA(X) "No data"
QUIT:X="" "Empty Set"
NEW S,I
SET S=0,I=1
FOR  QUIT:I>\$L(X,"^")  SET S=S+\$P(X,"^",I),I=I+1
QUIT (S/\$L(X,"^"))```
```USER>W \$\$MEAN^ROSETTA
No data
USER>W \$\$MEAN^ROSETTA("")
Empty Set
USER>

USER>W \$\$MEAN^ROSETTA("1^6^12^4")
5.75
```

## Nanoquery

```def sum(lst)
sum = 0
for n in lst
sum += n
end
return sum
end

def average(x)
return sum(x) / len(x)
end```

## Nemerle

```using System;
using System.Console;
using Nemerle.Collections;

module Mean
{
ArithmeticMean(x : list[int]) : double
{
|[] => 0.0
|_  =>(x.FoldLeft(0, _+_) :> double) / x.Length
}

Main() : void
{
WriteLine("Mean of [1 .. 10]: {0}", ArithmeticMean(\$[1 .. 10]));
}
}
```

## NetRexx

```/* NetRexx */
options replace format comments java crossref symbols nobinary

launchSample()
return

-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
method arithmeticMean(vv = Vector) public static signals DivideException returns Rexx
sum = 0
n_ = Rexx
loop n_ over vv
sum = sum + n_
end n_
mean = sum / vv.size()

return mean

-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
method launchSample() public static
TRUE_  = 1 == 1
FALSE_ = \TRUE_
tracing = FALSE_
vectors = getSampleData()
loop v_ = 0 to vectors.length - 1
say 'Average of:' vectors[v_].toString()
do
say '          =' arithmeticMean(vectors[v_])
catch dex = DivideException
say 'Caught "Divide By Zero"; bypassing...'
if tracing then dex.printStackTrace()
catch xex = RuntimeException
say 'Caught unspecified run-time exception; bypassing...'
if tracing then xex.printStackTrace()
end
say
end v_
return

-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
method getSampleData() private static returns Vector[]
seed = 1066
rng = Random(seed)
vectors =[ -
Vector(Arrays.asList([Rexx 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])), -
Vector(), -
Vector(Arrays.asList([Rexx rng.nextInt(seed), rng.nextInt(seed), rng.nextInt(seed), rng.nextInt(seed), rng.nextInt(seed), rng.nextInt(seed)])), -
Vector(Arrays.asList([Rexx rng.nextDouble(), rng.nextDouble(), rng.nextDouble(), rng.nextDouble(), rng.nextDouble(), rng.nextDouble(), rng.nextDouble()])), -
Vector(Arrays.asList([Rexx '1.0', '2.0', 3.0])), -
Vector(Arrays.asList([Rexx '1.0', 'not a number', 3.0])) -
]
return vectors```

Output:

```Average of: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
= 5.5

Average of: []
Caught "Divide By Zero"; bypassing...

Average of: [294, 726, 945, 828, 1031, 825]
= 774.833333

Average of: [0.3318379308729921, 0.7612271993941618, 0.9517290891755477, 0.7687823629521795, 0.2201768257213939, 0.1083471020993242, 0.5158554699332363]
= 0.52256514

Average of: [1.0, 2.0, 3.0]
= 2

Average of: [1.0, not a number, 3.0]
Caught unspecified run-time exception; bypassing...

```

## NewLISP

```(define (Mean Lst)
(if (empty? Lst)
0
(/ (apply + Lst) (length Lst))))

(Mean (sequence 1 1000))-> 500
(Mean '()) -> 0
```

## Nial

in the standard way, mean is

```mean is / [sum, tally]

mean 6 2 4
= 4```

but it fails with 0 length vectors. so using a tally with a minimum value 1

```dtally is recur [ empty rest, 1 first, 1 first, plus, rest ]
mean is / [sum, dtally]

mean []
=0```

## Nim

Translation of: C
```import strutils

proc mean(xs: openArray[float]): float =
for x in xs:
result += x
result = result / float(xs.len)

var v = @[1.0, 2.0, 2.718, 3.0, 3.142]
for i in 0..5:
echo "mean of first ", v.len, " = ", formatFloat(mean(v), precision = 0)
if v.len > 0: v.setLen(v.high)```

Output:

```mean of first 5 = 2.372
mean of first 4 = 2.1795
mean of first 3 = 1.906
mean of first 2 = 1.5
mean of first 1 = 1
mean of first 0 = -1.#IND```

## Niue

```[ [ , len 1 - at ! ] len 3 - times swap , ] 'map ; ( a Lisp like map, to sum the stack )
[ len 'n ; [ + ] 0 n swap-at map n / ] 'avg ;

1 2 3 4 5 avg .
=> 3
3.4 2.3 .01 2.0 2.1 avg .
=> 1.9619999999999997```

## Oberon-2

Oxford Oberon-2

```MODULE AvgMean;
IMPORT Out;
CONST MAXSIZE = 10;
PROCEDURE Avg(a: ARRAY OF REAL; items: INTEGER): REAL;
VAR
i: INTEGER;
total: REAL;
BEGIN
total := 0.0;
FOR i := 0 TO LEN(a) -  1 DO
total := total + a[i]
END;
END Avg;
VAR
ary: ARRAY MAXSIZE OF REAL;
BEGIN
ary := 10.0;
ary := 11.01;
ary := 12.02;
ary := 13.03;
ary := 14.04;
ary := 15.05;
ary := 16.06;
ary := 17.07;
ary := 18.08;
ary := 19.09;
Out.Fixed(Avg(ary),4,2);Out.Ln
END AvgMean.```

Output:

```14.55
```

## Objeck

```function : native : PrintAverage(values : FloatVector) ~ Nil {
values->Average()->PrintLine();
}```

## OCaml

These functions return a float:

```let mean_floats = function
| [] -> 0.
| xs -> List.fold_left (+.) 0. xs /. float_of_int (List.length xs)

let mean_ints xs = mean_floats (List.map float_of_int xs)```

the previous code is easier to read and understand, though if you wish the fastest implementation to use in production code notice several points: it is possible to save a call to List.length computing the length through the List.fold_left, and for mean_ints it is possible to save calling float_of_int on every numbers, converting only the result of the addition. (also when using List.map and when the order is not important, you can use List.rev_map instead to save an internal call to List.rev). Also the task asks to return 0 on empty lists, but in OCaml this case would rather be handled by an exception.

```let mean_floats xs =
if xs = [] then
invalid_arg "empty list"
else
let total, length =
List.fold_left
(fun (tot,len) x -> (x +. tot), len +. 1.)
(0., 0.) xs
in
(total /. length)
;;

let mean_ints xs =
if xs = [] then
invalid_arg "empty list"
else
let total, length =
List.fold_left
(fun (tot,len) x -> (x + tot), len +. 1.)
(0, 0.) xs
in
(float total /. length)
;;```

## Octave

GNU Octave has a mean function (from statistics package), but it does not handle an empty vector; an implementation that allows that is:

```function m = omean(l)
if ( numel(l) == 0 )
m = 0;
else
m = mean(l);
endif
endfunction

disp(omean([]));
disp(omean([1,2,3]));```

If the data contains missing value, encoded as non-a-number:

```function m = omean(l)
n = sum(~isnan(l));
l(isnan(l))=0;
s = sum(l);
m = s./n;
end;```

## Oforth

```: avg ( x -- avg )
x sum
x size dup ifZero: [ 2drop null ] else: [ >float / ]
;```
Output:
```[1, 2, 2.718, 3, 3.142] avg .
2.372 ok
[ ] avg .
null ok
```

## ooRexx

```call testAverage .array~of(10, 9, 8, 7, 6, 5, 4, 3, 2, 1)
call testAverage .array~of(10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, 0, 0, 0, .11)
call testAverage .array~of(10, 20, 30, 40, 50, -100, 4.7, -11e2)
call testAverage .array~new

::routine testAverage
use arg numbers
say "numbers =" numbers~toString("l", ", ")
say "average =" average(numbers)
say

::routine average
use arg numbers
-- return zero for an empty list
if numbers~isempty then return 0

sum = 0
do number over numbers
sum += number
end
return sum/numbers~items```

Output:

```numbers = 10, 9, 8, 7, 6, 5, 4, 3, 2, 1
average = 5.5

numbers = 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, 0, 0, 0, .11
average = 3.674

numbers = 10, 20, 30, 40, 50, -100, 4.7, -1100
average = -130.6625

numbers =
average = 0
```

## Oz

A version working on floats:

```declare
fun {Mean Xs}
{FoldL Xs Number.'+' 0.0} / {Int.toFloat {Length Xs}}
end
in
{Show {Mean [3. 1. 4. 1. 5. 9.]}}```

## PARI/GP

```avg(v)={
if(#v,vecsum(v)/#v)
};```

## Pascal

```Program Mean;

function DoMean(vector: array of double): double;
var
sum: double;
i, len: integer;
begin
sum := 0;
len := length(vector);
if len > 0 then
begin
for i := low(vector) to high(vector) do
sum := sum + vector[i];
sum := sum / len;
end;
DoMean := sum;
end;

const
vector: array [3..8] of double = (3.0, 1.0, 4.0, 1.0, 5.0, 9.0);
var
i: integer;
begin
writeln('Calculating the arithmetic mean of a series of numbers:');
write('Numbers: [ ');
for i := low(vector) to high(vector) do
write (vector[i]:3:1, ' ');
writeln (']');
writeln('Mean: ', DoMean(vector):10:8);
end.```

Output:

```Calculating the arithmetic mean of a series of numbers:
Numbers: [ 3.0 1.0 4.0 1.0 5.0 9.0 ]
Mean: 3.83333333
```

Alternative version using the Math unit:

```Program DoMean;
uses math;
const
vector: array [3..8] of double = (3.0, 1.0, 4.0, 1.0, 5.0, 9.0);
var
i: integer;
mean: double;
begin
writeln('Calculating the arithmetic mean of a series of numbers:');
write('Numbers: [ ');
for i := low(vector) to high(vector) do
write (vector[i]:3:1, ' ');
writeln (']');
mean := 0;
if length(vector) > 0 then
mean := sum(vector)/length(vector);
writeln('Mean: ', mean:10:8);
end.```

## Perl

```sub avg {
@_ or return 0;
my \$sum = 0;
\$sum += \$_ foreach @_;
return \$sum/@_;
}

print avg(qw(3 1 4 1 5 9)), "\n";```

## Phix

```with javascript_semantics
function mean(sequence s)
if length(s)=0 then return 0 end if
return sum(s)/length(s)
end function

? mean({1, 2, 5, -5, -9.5, 3.14159})
```

## Phixmonti

```1 2 5 -5 -9.5 3.14159 stklen tolist
len swap sum swap / print```

## PHP

```\$nums = array(3, 1, 4, 1, 5, 9);
if (\$nums)
echo array_sum(\$nums) / count(\$nums), "\n";
else
echo "0\n";```

## Picat

```mean([]) = false.
mean(V) = sum(V) / len(V).```

## PicoLisp

```(de mean (Lst)
(if (atom Lst)
0
(/ (apply + Lst) (length Lst)) ) )```

Output:

```: (mean (range 1 1000))
-> 500```

## PL/I

`arithmetic_mean = sum(A)/dimension(A,1);`

## Plain English

```To run:
Start up.
Demonstrate finding the arithmetic mean.
Wait for the escape key.
Shut down.

An entry is a thing with a fraction.
A list is some entries.
A sum is a fraction.
A mean is a fraction.

To demonstrate finding the arithmetic mean:
Create an example list.
Write "A list: " then the example list on the console.
Find a mean of the example list.
Write "The list's mean: " then the mean on the console.
Destroy the example list.

To add a fraction to a list:
Allocate memory for an entry.
Put the fraction into the entry's fraction.
Append the entry to the list.

To create an example list:
Add 1/1 to the example list.
Add 2/1 to the example list.
Add 5-1/3 to the example list.
Add 7-1/2 to the example list.

To find a sum of a list:
Put 0 into the sum.
Get an entry from the list.
Loop.
If the entry is nil, exit.
Add the entry's fraction to the sum.
Put the entry's next into the entry.
Repeat.

To find a mean of a list:
Find a sum of the list.
Put the sum divided by the list's count into the mean.

To convert a list to a string:
Get an entry from the list.
Loop.
If the entry is nil, break.
Append the entry's fraction to the string.
If the entry's next is not nil, append ", " to the string.
Put the entry's next into the entry.
Repeat.```
Output:
```A list: 1, 2, 5-1/3, 7-1/2
The list's mean: 3-23/24
```

## Pop11

```define mean(v);
lvars n = length(v), i, s = 0;
if n = 0 then
return(0);
else
for i from 1 to n do
s + v(i) -> s;
endfor;
endif;
return(s/n);
enddefine;```

## PostScript

```/findmean{
/x exch def
/sum 0 def
/i 0 def
x length 0 eq
{}
{
x length{
/sum sum x i get add def
}repeat
/sum sum x length div def
}ifelse
sum ==
}def```
Library: initlib
Works with: Ghostscript
```/avg {
dup length
{0 gt} {
exch 0 {add} fold exch div
} {
exch pop
} ifte
}.```

## PowerShell

The hard way by calculating a sum and dividing:

```function mean (\$x) {
if (\$x.Count -eq 0) {
return 0
} else {
\$sum = 0
foreach (\$i in \$x) {
\$sum += \$i
}
return \$sum / \$x.Count
}
}```

or, shorter, by using the `Measure-Object` cmdlet which already knows how to compute an average:

```function mean (\$x) {
if (\$x.Count -eq 0) {
return 0
} else {
return (\$x | Measure-Object -Average).Average
}
}```

## Processing

```float mean(float[] arr) {
float out = 0;
for (float n : arr) {
out += n;
}
return out / arr.length;
}```

## Prolog

Works with: SWI-Prolog version 6.6
```mean(List, Mean) :-
length(List, Length),
sumlist(List, Sum),
Mean is Sum / Length.```

## PureBasic

```Procedure.d mean(List number())
Protected sum=0

ForEach number()
sum + number()
Next
ProcedureReturn sum / ListSize(number())
; Depends on programm if zero check needed, returns nan on division by zero
EndProcedure```

## Python

Works with: Python version 3.0
.
Works with: Python version 2.6

Uses fsum which tracks multiple partial sums to avoid losing precision

```from math import fsum
def average(x):
return fsum(x)/float(len(x)) if x else 0
print (average([0,0,3,1,4,1,5,9,0,0]))
print (average([1e20,-1e-20,3,1,4,1,5,9,-1e20,1e-20]))```
Output:
```2.3
2.3```

Works with: Python version 2.5
```def average(x):
return sum(x)/float(len(x)) if x else 0
print (average([0,0,3,1,4,1,5,9,0,0]))
print (average([1e20,-1e-20,3,1,4,1,5,9,-1e20,1e-20]))```
Output:

(Notice how the second call gave the wrong result)

```2.3
1e-21```

Works with: Python version 2.4
```def avg(data):
if len(data)==0:
return 0
else:
return sum(data)/float(len(data))
print avg([0,0,3,1,4,1,5,9,0,0])```
Output:
`2.3`
Works with: Python version 3.4

Since 3.4, Python has a [statistics library in the stdlib, which takes care of these precision overflow issues in a way that works for all standard types, not just float, even with values way too big or small to fit in a float. (For Python 2.6-2.7, there's a backport available on PyPI.)

```>>> from statistics import mean
>>> mean([1e20,-1e-20,3,1,4,1,5,9,-1e20,1e-20])
2.3
>>> mean([10**10000, -10**10000, 3, 1, 4, 1, 5, 9, 0, 0])
2.3
>>> mean([10**10000, -10**10000, 3, 1, 4, 1, 5, 9, Fraction(1, 10**10000), Fraction(-1, 10**10000)])
Fraction(23, 10)
>>> big = 10**10000
>>> mean([Decimal(big), Decimal(-big), 3, 1, 4, 1, 5, 9, 1/Decimal(big), -1/Decimal(big)])
Decimal('2.3')```

## Q

A built-in solution is avg. An implementation of it could be:

`mean:{(sum x)%count x}`

## Quackery

Using the Quackery big number rational arithmetic library `bigrat.qky`.

```  [ \$ 'bigrat.qky' loadfile ] now!

[ [] swap times
[ 20001 random 10000 -
n->v 100 n->v v/
join nested join ] ]   is makevector   (   --> [   )

[ witheach
[ unpack
2 point\$ echo\$
i 0 > if
[ say ", " ] ] ]   is echodecs      ( [ -->     )

[ dup size n->v rot
0 n->v rot
witheach
[ unpack v+ ]
2swap v/ ]               is arithmean    ( [ --> n/d )

[ 5 makevector

say "Internal representation of a randomly generated vector" cr
say "of five rational numbers. They are distributed between" cr
say "-100.00 and +100.00 and are multiples of 0.01."
cr cr dup echo cr cr
say "Shown as decimal fractions."
cr cr dup echodecs cr cr

arithmean

say "Arithmetic mean of vector as a decimal fraction to" cr
say "5 places after the point, as a rounded proper" cr
say "fraction with the denominator not exceeding 10, and" cr
say "finally as a vulgar fraction without rounding." cr cr
2dup 5 point\$ echo\$
say ", "
2dup proper 10 round improper
proper\$ echo\$
say ", "
vulgar\$ echo\$ cr cr

say "The same, but with a vector of 9973 rational numbers," cr
say "20 decimal places and a denominator not exceeding 100." cr cr

9973 makevector arithmean

2dup 20 point\$ echo\$
say ", "
2dup proper 100 round improper
proper\$ echo\$
say ", "
vulgar\$ echo\$ cr ]       is demonstrate  (   -->     )```
Output:
```Internal representation of a randomly generated vector
of five rational numbers. They are distributed between
-100.00 and +100.00 and are multiples of 0.01.

[ [ -1999 100 ] [ 253 50 ] [ 2867 50 ] [ 3929 50 ] [ -25 2 ] ]

Shown as decimal fractions.

-19.99, 5.06, 57.34, 78.58, -12.5

Arithmetic mean of vector as a decimal fraction to
5 places after the point, as a rounded proper
fraction with the denominator not exceeding 10, and
finally as a vulgar fraction without rounding.

21.698, 21 7/10, 10849/500

The same, but with a vector of 9973 rational numbers,
20 decimal places and a denominator not exceeding 100.

-0.41664995487817106187, -5/12, -16621/39892```

## R

R has its mean function but it does not allow for NULL (void vectors or whatever) as argument: in this case it raises a warning and the result is NA. An implementation that does not suppress the warning could be:

```omean <- function(v) {
m <- mean(v)
ifelse(is.na(m), 0, m)
}```

## Racket

Racket's math library (available in v5.3.2 and newer) comes with a mean function that works on arbitrary sequences.

```#lang racket
(require math)

(mean (in-range 0 1000)) ; -> 499 1/2
(mean '(2 2 4 4))        ; -> 3
(mean #(3 4 5 8))        ; -> 5```

## Raku

(formerly Perl 6)

Works with: Rakudo version 2015.10-11
```multi mean([]){ Failure.new('mean on empty list is not defined') }; # Failure-objects are lazy exceptions
multi mean (@a) { ([+] @a) / @a }```

## Rapira

```fun mean(arr)
sum := 0
for N from 1 to #arr do
sum := sum + arr[N]
od
return (sum / #arr)
end```

## REBOL

```rebol [
Title: "Arithmetic Mean (Average)"
URL: http://rosettacode.org/wiki/Average/Arithmetic_mean
]

average: func [v /local sum][
if empty? v [return 0]

sum: 0
forall v [sum: sum + v/1]
sum / length? v
]

; Note precision loss as spread increased.

print [mold x: [] "->" average x]
print [mold x: [3 1 4 1 5 9] "->" average x]
print [mold x: [1000 3 1 4 1 5 9 -1000] "->" average x]
print [mold x: [1e20 3 1 4 1 5 9 -1e20] "->" average x]```

Output:

```[] -> 0
[3 1 4 1 5 9] -> 3.83333333333333
[1000 3 1 4 1 5 9 -1000] -> 2.875
[1E+20 3 1 4 1 5 9 -1E+20] -> 0.0```

## Red

Red comes with the `average` function.

```Red ["Arithmetic mean"]

print average []
print average [2 3 5]```
Output:
```none
3.333333333333334
```

The source code for `average`:

```average: func [
"Returns the average of all values in a block"
block [block! vector! paren! hash!]
][
if empty? block [return none]
divide sum block to float! length? block
]```

## ReScript

```let arr = [3, 8, 4, 1, 5, 12]

let num = Js.Array.length(arr)
let tot = Js.Array.reduce(\"+", 0, arr)
let mean = float_of_int(tot) /. float_of_int(num)

Js.log(Js.Float.toString(mean))```
Output:
```\$ bsc arithmean.res > arithmean.js
\$ node arithmean.js
5.5
```

## REXX

The vectors (list) can contain any valid (REXX) numbers.

A check is made to validate if the numbers in the list are all numeric.

```/*REXX program finds the averages/arithmetic mean of several lists (vectors) or CL input*/
parse arg @.1; if @.1=''  then do;   #=6                         /*vector from the C.L.?*/
@.1 =   10 9 8 7 6 5 4 3 2 1
@.2 =   10 9 8 7 6 5 4 3 2 1 0 0 0 0  .11
@.3 =  '10 20 30 40 50  -100  4.7  -11e2'
@.4 =  '1 2 3 4  five  6 7 8 9  10.1.  ±2'
@.5 =  'World War I  &  World War II'
@.6 =                             /*  ◄─── a null value. */
end
else #=1                               /*number of CL vectors.*/
do j=1  for #
say '       numbers = '   @.j
say '       average = '   avg(@.j)
say copies('═', 79)
end   /*t*/
exit                                             /*stick a fork in it,  we're all done. */
/*──────────────────────────────────────────────────────────────────────────────────────*/
avg: procedure;  parse arg x;     #=words(x)                      /*#:  number of items.*/
if #==0  then return  'N/A: ───[null vector.]'               /*No words? Return N/A*/
\$=0
do k=1  for #;      _=word(x,k)                         /*obtain a number.    */
if datatype(_,'N')  then do;  \$=\$+_;  iterate;   end    /*if numeric, then add*/
say left('',40) "***error***  non-numeric: " _;  #=#-1  /*error; adjust number*/
end   /*k*/

if #==0  then return  'N/A: ───[no numeric values.]'         /*No nums?  Return N/A*/
return \$ / #                                                 /*return the average. */```

output   when using the (internal) lists:

```       numbers =  10 9 8 7 6 5 4 3 2 1
average =  5.5
═══════════════════════════════════════════════════════════════════════════════
numbers =  10 9 8 7 6 5 4 3 2 1 0 0 0 0 .11
average =  3.674
═══════════════════════════════════════════════════════════════════════════════
numbers =  10 20 30 40 50  -100  4.7  -11e2
average =  -130.6625
═══════════════════════════════════════════════════════════════════════════════
numbers =  1 2 3 4  five  6 7 8 9  10.1.  ±2
***error***  non-numeric:  five
***error***  non-numeric:  10.1.
***error***  non-numeric:  ±2
average =  5
═══════════════════════════════════════════════════════════════════════════════
numbers =  World War I  &  World War II
***error***  non-numeric:  World
***error***  non-numeric:  War
***error***  non-numeric:  I
***error***  non-numeric:  &
***error***  non-numeric:  World
***error***  non-numeric:  War
***error***  non-numeric:  II
average =  N/A: ───[no numeric values.]
═══════════════════════════════════════════════════════════════════════════════
numbers =
average =  N/A: ───[null vector.]
═══════════════════════════════════════════════════════════════════════════════

```

## Ring

```nums = [1,2,3,4,5,6,7,8,9,10]
sum = 0
see "Average = " + average(nums) + nl

func average numbers
for i = 1 to len(numbers)
sum = sum + nums[i]
next
return sum/len(numbers)```

## RPL

This is a simple rewrite of the dc version above. This works on an HP 48. "→" is a single right arrow character on the 48. Feel free to alter this code as necessary to work on RPL/2.

```1 2 3 5 7
AMEAN
<< CLEAR DEPTH DUP 'N' STO →LIST ΣLIST N / >>
3.6```

### Hard-working approach

Works for all RPL versions.

```≪ DUP SIZE SWAP OVER
0 1 ROT FOR j
OVER j GET + NEXT
ROT / SWAP DROP
≫
```

### Hard-working approach with local variables

No significant impact on program size or speed, but much more readable

```≪ DUP SIZE → vector n
≪  0 1 n FOR j
vector j GET + NEXT
n /
≫ ≫
```

### Straightforward approach

The dot product of any vector with [1 1 ... 1] gives the sum of its elements.

```≪ SIZE LAST DUP 1 CON DOT SWAP / ≫
'AMEAN' STO
```

### Using built-in statistics features

Most of the code is dedicated to store the input array according to built-in statistics requirements, which requires a matrix with one line per record. Main benefit of this approach is that you can then easily calculate standard deviation and variance by calling resp. `SDEV` and `VAR` functions.

```≪ { 1 } OVER SIZE + RDM TRN '∑DAT' STO MEAN ≫ 'AMEAN' STO
```
```[ 1 5 0 -4 6 ] AMEAN
```
Output:
```1: 1.6
```

## Ruby

```def mean(nums)
nums.sum(0.0) / nums.size
end

nums = [3, 1, 4, 1, 5, 9]
nums.size.downto(0) do |i|
ary = nums[0,i]
puts "array size #{ary.size} : #{mean(ary)}"
end```
Output:
```array size 6 : 3.8333333333333335
array size 5 : 2.8
array size 4 : 2.25
array size 3 : 2.6666666666666665
array size 2 : 2.0
array size 1 : 3.0
array size 0 : NaN
```

## Run BASIC

```print "Gimme the number in the array:";input numArray
dim value(numArray)
for i = 1 to numArray
value(i) = i * 1.5
next

for i = 1 to total
totValue = totValue +value(numArray)
next
if totValue <> 0 then mean = totValue/numArray
print "The mean is: ";mean```

## Rust

```fn sum(arr: &[f64]) -> f64 {
arr.iter().fold(0.0, |p,&q| p + q)
}

fn mean(arr: &[f64]) -> f64 {
sum(arr) / arr.len() as f64
}

fn main() {
let v = &[2.0, 3.0, 5.0, 7.0, 13.0, 21.0, 33.0, 54.0];
println!("mean of {:?}: {:?}", v, mean(v));

let w = &[];
println!("mean of {:?}: {:?}", w, mean(w));
}```

Output:

```mean of [2, 3, 5, 7, 13, 21, 33, 54]: 17.25
mean of []: NaN```

## Sather

Built to work with VEC, ("geometric" vectors), whose elements must be floats. A 0-dimension vector yields "nan".

```class VECOPS is
mean(v:VEC):FLT is
m ::= 0.0;
loop m := m + v.aelt!; end;
return m / v.dim.flt;
end;
end;

class MAIN is
main is
v ::= #VEC(|1.0, 5.0, 7.0|);
#OUT + VECOPS::mean(v) + "\n";
end;
end;```

## Scala

Using Scala 2.7, this has to be defined for each numeric type:

`def mean(s: Seq[Int]) = s.foldLeft(0)(_+_) / s.size`

However, Scala 2.8 gives much more flexibility, but you still have to opt between integral types and fractional types. For example:

```def mean[T](s: Seq[T])(implicit n: Integral[T]) = {
import n._
s.foldLeft(zero)(_+_) / fromInt(s.size)
}```

This can be used with any subclass of Sequence on integral types, up to and including BigInt. One can also create singletons extending Integral for user-defined numeric classes. Likewise, Integral can be replaced by Fractional in the code to support fractional types, such as Float and Double.

Alas, Scala 2.8 also simplifies the task in another way:

`def mean[T](s: Seq[T])(implicit n: Fractional[T]) = n.div(s.sum, n.fromInt(s.size))`

Here we show a function that supports fractional types. Instead of importing the definitions from n, we are calling them on n itself. And because we did not import them, the implicit definitions that would allow us to use / were not imported as well. Finally, we use sum instead of foldLeft.

## Scheme

```(define (mean l)
(if (null? l)
0
(/ (apply + l) (length l))))```
```> (mean (list 3 1 4 1 5 9))
3 5/6
```

## Seed7

```\$ include "seed7_05.s7i";
include "float.s7i";

const array float: numVector is [] (1.0, 2.0, 3.0, 4.0, 5.0);

const func float: mean (in array float: numbers) is func
result
var float: result is 0.0;
local
var float: total is 0.0;
var float: num is 0.0;
begin
if length(numbers) <> 0 then
for num range numbers do
total +:= num;
end for;
result := total / flt(length(numbers));
end if;
end func;

const proc: main is func
begin
writeln(mean(numVector));
end func;```

## SenseTalk

SenseTalk has a built-in average function.

```put the average of [12,92,-17,66,128]

put average(empty)```
Output:
```56.2
nan
```

## Sidef

```func avg(Array list) {
list.len > 0 || return 0;
list.sum / list.len;
}

say avg([Math.inf, Math.inf]);
say avg([3,1,4,1,5,9]);
say avg([1e+20, 3, 1, 4, 1, 5, 9, -1e+20]);
say avg([10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, 0, 0, 0, 0.11]);
say avg([10, 20, 30, 40, 50, -100, 4.7, -1100]);```
Output:
```inf
3.833333333333333333333333333333333333333
2.875
3.674
-130.6625```

## Slate

```[|:list| (list reduce: #+ `er ifEmpty: ) / (list isEmpty ifTrue:  ifFalse: [list size])] applyWith: #(3 1 4 1 5 9).
[|:list| (list reduce: #+ `er ifEmpty: ) / (list isEmpty ifTrue:  ifFalse: [list size])] applyWith: {}.```

## Smalltalk

```| numbers |

numbers := #(1 2 3 4 5 6 7 8).
(numbers isEmpty
ifTrue:
ifFalse: [
(numbers inject: 0 into: [:sumSoFar :eachElement | sumSoFar + eachElement]) / numbers size ]
) displayNl.```

However, the empty check can be omitted, as inject returns the injected value for empty collections, and we probably do not care for the average of nothing (i.e. the division by zero exception):

```| numbers |

numbers := #(1 2 3 4 5 6 7 8).
( numbers inject: 0 into: [:sumSoFar :eachElement | sumSoFar + eachElement]) / numbers size] ) displayNl.```

also, most Smalltalk's collection classes already provide sum and average methods, which makes it:

Works with: Pharo
Works with: Smalltalk/X
```| numbers |

numbers := #(1 2 3 4 5 6 7 8).
(numbers sum / numbers size) displayNl.```

or

```| numbers |

numbers := #(1 2 3 4 5 6 7 8).
numbers average displayNl.```

## SNOBOL4

Works with: Macro Spitbol
Works with: Snobol4+
Works with: CSnobol
```        define('avg(a)i,sum') :(avg_end)
avg     i = i + 1; sum = sum + a<i> :s(avg)
avg = 1.0 * sum / prototype(a) :(return)
avg_end

*       # Fill arrays
str = '1 2 3 4 5 6 7 8 9 10'; arr = array(10)
loop    i = i + 1; str len(p) span('0123456789') . arr<i> @p :s(loop)
empty = array(1) ;* Null vector

*       # Test and display
output = '[' str '] -> ' avg(arr)
output = '[ ] -> ' avg(empty)
end```

Output:

```[1 2 3 4 5 6 7 8 9 10] -> 5.5
[ ] -> 0.```

## SQL

Tested on Oracle 11gR2, the more limited the tool, the more resourceful one becomes :)

```create table "numbers" ("datapoint" integer);

insert into "numbers" select rownum from tab;

select sum("datapoint")/count(*)  from "numbers";```

...or...

`select avg("datapoint") from "numbers";`

## Standard ML

These functions return a real:

```fun mean_reals [] = 0.0
| mean_reals xs = foldl op+ 0.0 xs / real (length xs);

val mean_ints = mean_reals o (map real);```

The previous code is easier to read and understand, though if you want the fastest implementation to use in production code notice several points: it is possible to save a call to `length` computing the length through the `foldl`, and for mean_ints it is possible to save calling `real` on every numbers, converting only the result of the addition. Also the task asks to return 0 on empty lists, but in Standard ML this case would rather be handled by an exception.

```fun mean_reals [] = raise Empty
| mean_reals xs = let
val (total, length) =
foldl
(fn (x, (tot,len)) => (x + tot, len + 1.0))
(0.0, 0.0) xs
in
(total / length)
end;

fun mean_ints [] = raise Empty
| mean_ints xs = let
val (total, length) =
foldl
(fn (x, (tot,len)) => (x + tot, len + 1.0))
(0, 0.0) xs
in
(real total / length)
end;```

## Stata

### Mean of a dataset variable

Illustration of the mean on the population (in millions) in january 2016 of a few european countries (source Eurostat).

```clear all
input str20 country population
Belgium 11311.1
Bulgaria 7153.8
"Czech Republic" 10553.8
Denmark 5707.3
Germany 82175.7
Estonia 1315.9
Ireland 4724.7
Greece 10783.7
end

. mean population

Mean estimation                   Number of obs   =          8

--------------------------------------------------------------
|       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
population |   16715.75   9431.077     -5585.203     39016.7
--------------------------------------------------------------

. tabstat population, statistic(mean)
variable |      mean
-------------+----------
population |  16715.75
------------------------

. quietly summarize population
. display r(mean)
16715.75```

### Mean in Mata

```mata
a=11311.1\7153.8\10553.8\5707.3\
82175.7\1315.9\4724.7\10783.7

mean(a)
16715.75```

## Swift

```func meanDoubles(s: [Double]) -> Double {
return s.reduce(0, +) / Double(s.count)
}
func meanInts(s: [Int]) -> Double {
return meanDoubles(s.map{Double(\$0)})
}```

## Tcl

```package require Tcl 8.5
proc mean args {
if {[set num [llength \$args]] == 0} {return 0}
expr {[tcl::mathop::+ {*}\$args] / double(\$num)}
}
mean 3 1 4 1 5 9 ;# ==> 3.8333333333333335```

## TI-83 BASIC

`Mean(Ans`

## TI-89 BASIC

`Define rcmean(nums) = when(dim(nums) = 0, 0, mean(nums))`

## Trith

```: mean dup empty? [drop 0] [dup [+] foldl1 swap length /] branch ;

[3 1 4 1 5 9] mean```

## TypeScript

```function mean(numbersArr)
{
let arrLen = numbersArr.length;
if (arrLen > 0) {
let sum: number = 0;
for (let i of numbersArr) {
sum += i;
}
return sum/arrLen;
}
else return "Not defined";
}

## UNIX Shell

1) First solution with bash (V >= 3), works with floats :

`echo "`cat f | paste -sd+ | bc -l` / `cat f | wc -l`" | bc -l`
```cat f
1
2
4
8
16
-200

echo "`cat f | paste -sd+ | bc -l`/`cat f | wc -l`" | bc -l
-28.16666666666666666666

cat f
1.109434
2
4.5
8.45
16
-200
400.56

echo "`cat f | paste -sd+ | bc -l`/`cat f | wc -l`" |bc -l
33.23134771428571428571```

2) This example uses expr, so it only works with integers. It checks that each string in the list is an integer.

```mean() {
if expr \$# >/dev/null; then
(count=0
sum=0
while expr \$# \> 0 >/dev/null; do
sum=`expr \$sum + "\$1"`
result=\$?
expr \$result \> 1 >/dev/null && exit \$result

count=`expr \$count + 1`
shift
done
expr \$sum / \$count)
else
echo 0
fi
}

printf "test 1: "; mean				# 0
printf "test 2: "; mean 300			# 300
printf "test 3: "; mean 300 100 400		# 266
printf "test 4: "; mean -400 400 -1300 200	# -275
printf "test 5: "; mean -			# expr: syntax error
printf "test 6: "; mean 1 2 A 3			# expr: non-numeric argument```

## UnixPipes

 This example is incorrect. Please fix the code and remove this message.Details: There is a race between parallel commands. `cat count` might try to read the file before `wc -l >count` writes it. This may cause an error like cat: count: No such file or directory, then bc: stdin:1: syntax error: ) unexpected.

Uses ksh93-style process substitution. Also overwrites the file named count in the current directory.

Works with: bash
```term() {
b=\$1;res=\$2
echo "scale=5;\$res+\$b" | bc
}

sum() {
test -n "\$B" && (term \$B \$res) || (term 0 \$res))
}

fold() {
func=\$1
fold \$func | \$func \$a
done)
}

mean() {
tee >(wc -l > count) | fold sum | xargs echo "scale=5;(1/" \$(cat count) ") * " | bc
}

(echo 3; echo 1; echo 4) | mean```

## Ursa

```#
# arithmetic mean
#

decl int<> input
decl int i
for (set i 1) (< i (size args)) (inc i)
append (int args<i>) input
end for

out (/ (+ input) (size input)) endl console```

## Ursala

There is a library function for means already, although it doesn't cope with empty vectors. A mean function could be defined as shown for this task.

```#import nat
#import flo

mean = ~&?\0.! div^/plus:-0. float+ length

#cast %e

example = mean <5.,3.,-2.,6.,-4.>```

output:

`1.600000e+00`

## V

```[mean
[sum 0 [+] fold].
dup sum
swap size [[1 <] ] when /
].```

## Vala

Using array to hold the numbers of the list:

```double arithmetic(double[] list){
double mean;
double sum = 0;

if (list.length == 0)
return 0.0;
foreach(double number in list){
sum += number;
} // foreach

mean = sum / list.length;

return mean;
} // end arithmetic mean

public static void main(){
double[] test = {1.0, 2.0, 5.0, -5.0, 9.5, 3.14159};
double[] zero_len = {};

double mean = arithmetic(test);
double mean_zero = arithmetic(zero_len);

stdout.printf("%s\n", mean.to_string());
stdout.printf("%s\n", mean_zero.to_string());
}```

Output:

```2.6069316666666666
0
```

## VBA

```Private Function mean(v() As Double, ByVal leng As Integer) As Variant
Dim sum As Double, i As Integer
sum = 0: i = 0
For i = 0 To leng - 1
sum = sum + vv
Next i
If leng = 0 Then
mean = CVErr(xlErrDiv0)
Else
mean = sum / leng
End If
End Function
Public Sub main()
Dim v(4) As Double
Dim i As Integer, leng As Integer
v(0) = 1#
v(1) = 2#
v(2) = 2.178
v(3) = 3#
v(4) = 3.142
For leng = 5 To 0 Step -1
Debug.Print "mean[";
For i = 0 To leng - 1
Debug.Print IIf(i, "; " & v(i), "" & v(i));
Next i
Debug.Print "] = "; mean(v, leng)
Next leng
End Sub```
Output:
```mean[1; 2; 2,178; 3; 3,142] =  0
mean[1; 2; 2,178; 3] =  0
mean[1; 2; 2,178] =  0
mean[1; 2] =  0
mean =  0
mean[] = Fout 2007```

## VBScript

```Function mean(arr)
size = UBound(arr) + 1
mean = 0
For i = 0 To UBound(arr)
mean = mean + arr(i)
Next
mean = mean/size
End Function

'Example
WScript.Echo mean(Array(3,1,4,1,5,9))```
Output:
`3.83333333333333`

## Vedit macro language

The numeric data is stored in current edit buffer as ASCII strings, one value per line.

```#1 = 0			// Sum
#2 = 0			// Count
BOF
While(!At_EOF) {
#1 += Num_Eval(SIMPLE)
#2++
Line(1, ERRBREAK)
}
if (#2) { #1 /= #2 }
Num_Type(#1)```

## Vim Script

Throws an exception if the list is empty.

```function Mean(lst)
if empty(a:lst)
throw "Empty"
endif
let sum = 0.0
for i in a:lst
let sum += i
endfor
return sum / len(a:lst)
endfunction```

## V (Vlang)

```import math
import arrays

fn main() {
for v in [
[]f64{},                         // mean returns ok = false
[math.inf(1), math.inf(1)], // answer is +Inf

// answer is NaN, and mean returns ok = true, indicating NaN
// is the correct result
[math.inf(1), math.inf(-1)],

[f64(3), 1, 4, 1, 5, 9],

[f64(10), 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, 0, 0, 0, .11],
[f64(10), 20, 30, 40, 50, -100, 4.7, -11e2],
] {
println("Vector: \$v")
m := arrays.fold(v, 0.0, fn(r f64, v f64) f64 { return r+v })/v.len
println("Mean of \$v.len numbers is \$m\n")
}
}```
Output:
```Vector: []
Mean of 0 numbers is nan

Vector: [+inf, +inf]
Mean of 2 numbers is +inf

Vector: [+inf, -inf]
Mean of 2 numbers is nan

Vector: [3, 1, 4, 1, 5, 9]
Mean of 6 numbers is 3.8333333333333335

Vector: [10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, 0, 0, 0, 0.11]
Mean of 15 numbers is 3.674

Vector: [10, 20, 30, 40, 50, -100, 4.7, -1100]
Mean of 8 numbers is -130.66```

## Wart

```def (mean l)
sum.l / len.l```

Example run:

```mean '(1 2 3)
=> 2```

## WDTE

```let s => import 'stream';
let a => import 'arrays';

let mean nums =>
a.stream nums
-> s.reduce [0; 0] (@ s p n => [+ (a.at p 0) 1; + (a.at p 1) n])
-> (@ s p => / (a.at p 1) (a.at p 0));```

This is a tad messier than it has to be due to a lack of a way to get the length of an array in WDTE currently.

Usage:

`mean [1; 2; 3] -- io.writeln io.stdout;`

Output:

`2`

## Wortel

```@let {
; using a fork (sum divided-by length)
mean1 @(@sum / #)

; using a function with a named argument
mean2 &a / @sum a #a

[[
!mean1 [3 1 4 1 5 9 2]
!mean2 [3 1 4 1 5 9 2]
]]
}```

Returns:

`[3.5714285714285716 3.5714285714285716]`

## Wren

```class Arithmetic {
static mean(arr) {
if (arr.count == 0) Fiber.abort("Length must be greater than zero")
return arr.reduce(Fn.new{ |x,y| x+y }) / arr.count
}
}
Arithmetic.mean([1,2,3,4,5]) // 3```

## XLISP

The specification calls for a function that takes a vector; for convenience, we convert this vector internally to a list. The mean of a zero-length vector is returned as nil, equivalent to the empty list or logical false.

```(defun mean (v)
(if (= (vector-length v) 0)
nil
(let ((l (vector->list v)))
(/ (apply + l) (length l)))))```

## XPL0

```code CrLf=9;
code real RlOut=48;

func real Mean(A, N);
real A;  int N;
real S;  int I;
[if N=0 then return 0.0;
S:= 0.0;
for I:= 0 to N-1 do
S:= S+A(I);
return S/float(N);
]; \Mean

real Test;
[Test:= [1.0, 2.0, 5.0, -5.0, 9.5, 3.14159];
RlOut(0, Mean(Test, 6));  CrLf(0);
]```

Output:

```    2.60693
```

## XSLT

Where `\$values` is some variable indicating a set of nodes containing numbers, the average is given by the XPath expression:

`sum(\$values) div count(\$values)`

### Runnable example

```<xsl:stylesheet xmlns:xsl="http://www.w3.org/1999/XSL/Transform" version="1.0">
<xsl:output method="text"/>

<xsl:template match="/">
<xsl:variable name="values" select="/*/*"/>
<xsl:value-of select="sum(\$values) div count(\$values)"/>
</xsl:template>
</xsl:stylesheet>```

Sample input:

```<numbers>
<!-- Average is 2.4 -->
<number>1</number>
<number>1</number>
<number>2</number>
<number>3</number>
<number>5</number>
</numbers>```

## Yorick

```func mean(x) {
if(is_void(x)) return 0;
return x(*)(avg);
}```

## zkl

Converts int to floats (implicitly):

```fcn mean(a,b,c,etc){ z:=vm.arglist; z.reduce('+,0.0)/z.len() }
mean(3,1,4,1,5,9);  //-->3.83333
mean(); //-->Exception thrown: MathError(NaN (Not a number))```

To pass in a vector/list:

```fcn meanV(z){ z.reduce('+,0.0)/z.len() }
meanV(T(3,1,4,1,5,9)); // --> 3.83333```

## Zoea

```program: average
case: 1
input: [2,3,10]
output: 5
case: 2
input: [7,11]
output: 9```

## zonnon

```module Averages;
type
Vector = array {math} * of real;

procedure ArithmeticMean(x: Vector): real;
begin
(* sum is a predefined function for mathematical arrays *)
return sum(x)
end ArithmeticMean;
var
x: Vector;

begin
x := new Vector(4);
x := [1.0, 2.3, 3.2, 2.1, 5.3];
write("arithmetic mean: ");writeln(ArithmeticMean(x):10:2)
end Averages.```
Output:
```arithmetic mean:       13,9
```