View source for Averages/Arithmetic mean

{{task|Probability and statistics}}

;Task

Write a program to find the [[wp:arithmetic mean|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.

{{task heading|See also}}

{{Related tasks/Statistical measures}}

<br><hr>

=={{header|0815}}==
<syntaxhighlight lang="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/%
</syntaxhighlight>
{{out}}
<pre>
0
D
</pre>

=={{header|11l}}==
{{trans|Python}}
<syntaxhighlight lang="11l">F average(x)
   R sum(x) / Float(x.len)

print(average([0, 0, 3, 1, 4, 1, 5, 9, 0, 0]))</syntaxhighlight>
{{out}}
<pre>
2.3
</pre>

=={{header|360 Assembly}}==
Compact and functional.
<syntaxhighlight lang="360asm">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</syntaxhighlight>
{{out}}
<pre>         5.50</pre>

=={{header|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.

<syntaxhighlight lang="6502asm">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

			DEY		;start with NumberInts-1
AddLoop:		LDA (ArrayPtr),Y
			CLC
			ADC Temp
			STA Temp
			LDA Temp+1
			ADC #0
			STA Temp+1
			DEY
			CPY #255
			BNE AddLoop

			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</syntaxhighlight>

=={{header|8th}}== 
<syntaxhighlight lang="forth">
: 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
</syntaxhighlight>
Output is:<br>
2.54395<br>
NaN<br>
10.00000

=={{header|ACL2}}==
<syntaxhighlight lang="lisp">(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))))</syntaxhighlight>

=={{header|Action!}}==
{{libheader|Action! Tool Kit}}
<syntaxhighlight lang="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)
    RealAdd(sum,x,tmp)
    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=[9]

  Put(125) PutE() ;clear screen
  Test(a1,6)
  Test(a2,5)
  Test(a3,1)
  Test(a3,0)
RETURN</syntaxhighlight>
{{out}}
[https://gitlab.com/amarok8bit/action-rosetta-code/-/raw/master/images/Arithmetic_mean.png Screenshot from Atari 8-bit computer]
<pre>
mean(1,2,3,4,5,6)=3.5
mean(1,10,100,1000,10000)=2222.2
mean(9)=9
mean()=0
</pre>

=={{header|ActionScript}}==
<syntaxhighlight lang="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;
}</syntaxhighlight>

=={{header|Ada}}==
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.
<syntaxhighlight lang="ada">with Ada.Float_Text_Io; use Ada.Float_Text_Io;
with Ada.Text_IO; use Ada.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;</syntaxhighlight>
Output:
3.83333

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

=={{header|Aime}}==
<syntaxhighlight lang="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;
}</syntaxhighlight>

=={{header|ALGOL 68}}==
{{trans|C}}

{{works with|ALGOL 68|Standard - no extensions to language used}}
{{works with|ALGOL 68G|Any - tested with release mk15-0.8b.fc9.i386}} 
{{works with|ELLA ALGOL 68|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.}}
<syntaxhighlight lang="algol68">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:(
  [6]REAL test := (1.0, 2.0, 5.0, -5.0, 9.5, 3.14159);
  print((mean(test),new line))
)</syntaxhighlight>

=={{header|ALGOL W}}==
<syntaxhighlight lang="algolw">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.</syntaxhighlight>

=={{header|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") 
<syntaxhighlight lang="amigae">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[20] : STRING
  WriteF('mean \s\n',
         RealF(s,mean([1.0, 2.0, 3.0, 4.0, 5.0]), 2))
ENDPROC</syntaxhighlight>

=={{header|AntLang}}==
AntLang has a built-in avg function.
<syntaxhighlight lang="antlang">avg[list]</syntaxhighlight>

=={{header|APL}}==
{{works with|APL2}}
<syntaxhighlight lang="apl">
      X←3 1 4 1 5 9
      (+/X)÷⍴X
3.833333333
</syntaxhighlight>

{{works with|Dyalog APL}}
A proper function definition:
<syntaxhighlight lang="apl">
      Avg←{(+⌿⍵)÷≢⍵}
      Avg 1 2 3 4 5 6
3.5
</syntaxhighlight>

Using [[tacit programming]]:
<syntaxhighlight lang="apl">
      Avg← +⌿÷≢
      Avg 1 2 3 4 5 6
3.5
</syntaxhighlight>
'''N.B.:''' the symbol for [https://aplwiki.com/wiki/Tally Tally (≢)] doesn't display correctly on Chrome-based browsers at the moment.

=={{header|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.

<syntaxhighlight lang="applescript">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})</syntaxhighlight>

{{output}}
<pre>2530.0</pre>

===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

<syntaxhighlight lang="applescript">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})</syntaxhighlight>

{{output}}
<pre>2530.0</pre>

=={{header|Applesoft BASIC}}==
<syntaxhighlight lang="applesoftbasic">REM COLLECTION IN DATA STATEMENTS, EMPTY DATA IS THE END OF THE COLLECTION
    0 READ V$
    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
    7     READ V$
    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
</syntaxhighlight>

=={{header|Arturo}}==

<syntaxhighlight lang="rebol">arr: [1 2 3 4 5 6 7]
 
print average arr</syntaxhighlight>

{{out}}

<pre>4.0</pre>

=={{header|Astro}}==
<syntaxhighlight lang="astro">mean([1, 2, 3])
mean(1..10)
mean([])
</syntaxhighlight>

=={{header|AutoHotkey}}==
<syntaxhighlight lang="autohotkey">i = 10
Loop, % i {
  Random, v, -3.141592, 3.141592
  list .= v "`n"
  sum += v
}
MsgBox, % i ? list "`nmean: " sum/i:0</syntaxhighlight>

=={{header|AWK}}==
<syntaxhighlight lang="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)
}
</syntaxhighlight>

{{out}}
<pre>
$ awk -f mean.awk 
3.92689
zero-length input !
</pre>

=={{header|Babel}}==

<syntaxhighlight lang="babel">(3 24 18 427 483 49 14 4294 2 41) dup len <- sum ! -> / itod <<</syntaxhighlight>

{{Out}}
<pre>535</pre>

=={{header|BASIC}}==
{{works with|QBasic}}

Assume the numbers are in an array named "nums".
<syntaxhighlight lang="qbasic">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</syntaxhighlight>

==={{header|BBC BASIC}}===
{{works with|BBC BASIC for Windows}}

To calculate the mean of an array:
<syntaxhighlight lang="bbc basic">
      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)
</syntaxhighlight>
[[User:MichaelHutton|Michael Hutton]] 14:02, 29 May 2011 (UTC)

==={{header|IS-BASIC}}===
<syntaxhighlight lang="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</syntaxhighlight>

=={{header|bc}}==
Uses the current scale for calculating the mean.
<syntaxhighlight lang="bc">define m(a[], n) {
    auto i, s

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

=={{header|Befunge}}==
The first input is the length of the vector. If a length of 0 is entered, the result is equal to <code>0/0</code>. 
<syntaxhighlight lang="befunge">&:0\:!v!:-1<
 @./\$_\&+\^</syntaxhighlight>

=={{header|blz}}==
<syntaxhighlight lang="blz">
:mean(vec)
    vec.fold_left(0, (x, y -> x + y)) / vec.length()
end</syntaxhighlight>

=={{header|Bracmat}}==
Here are two solutions. The first uses a while loop, the second scans the input by backtracking.
<syntaxhighlight lang="bracmat">
(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
);
</syntaxhighlight>
To test with a list of all numbers 1 .. 999999:
<syntaxhighlight lang="bracmat">
( :?test
& 1000000:?Length
& whl'(!Length+-1:?Length:>0&!Length !test:?test)
& out$mean1$!test
& out$mean2$!test
)</syntaxhighlight>

=={{header|Brat}}==
<syntaxhighlight lang="brat">mean = { list |
  true? list.empty?, 0, { list.reduce(0, :+) / list.length }
}

p mean 1.to 10  #Prints 5.5</syntaxhighlight>

=={{header|Burlesque}}==

<syntaxhighlight lang="burlesque">
blsq ) {1 2 2.718 3 3.142}av
2.372
blsq ) {}av
NaN
</syntaxhighlight>

=={{header|BQN}}==
Defines a tacit Avg function which works on any simple numeric list.

<syntaxhighlight lang="bqn">Avg ← +´÷≠

Avg 1‿2‿3‿4</syntaxhighlight>
<syntaxhighlight lang="text">2.5</syntaxhighlight>

[https://mlochbaum.github.io/BQN/try.html#code=QXZnIOKGkCArwrTDt+KJoAoKQXZnIDHigL8y4oC/M+KAvzQ= Try It!]

=={{header|C}}==
Compute mean of a <code>double</code> array of given length.  If length is zero, does whatever <code>0.0/0</code> does (usually means returning <code>NaN</code>).

<syntaxhighlight lang="c">#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;
}</syntaxhighlight>{{out}}<pre>
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] = 1
mean[] = -nan
</pre>

=={{header|C sharp|C#}}==
<syntaxhighlight lang="csharp">using System;
using System.Linq;

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

Alternative version (not using the built-in function):
<syntaxhighlight lang="csharp">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
        Console.ReadLine();
    }

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

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

=={{header|C++}}==
{{libheader|STL}}
<syntaxhighlight lang="cpp">#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();
}</syntaxhighlight>

Shorter (and more idiomatic) version:

<syntaxhighlight lang="cpp">#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();
}</syntaxhighlight>

Idiomatic version templated on any kind of iterator:

<syntaxhighlight lang="cpp">#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);
}</syntaxhighlight>

=={{header|Chef}}==

<syntaxhighlight lang="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.</syntaxhighlight>

=={{header|Clojure}}==

Returns a [http://clojure.org/data_structures ratio]:
<syntaxhighlight lang="lisp">(defn mean [sq]
  (if (empty? sq)
      0
      (/ (reduce + sq) (count sq))))</syntaxhighlight>

Returns a float:
<syntaxhighlight lang="lisp">(defn mean [sq]
  (if (empty? sq)
      0
      (float (/ (reduce + sq) (count sq)))))</syntaxhighlight>

=={{header|COBOL}}==
Intrinsic function:
<syntaxhighlight lang="cobol">FUNCTION MEAN(some-table (ALL))</syntaxhighlight>

Sample implementation:
<syntaxhighlight lang="cobol">       IDENTIFICATION DIVISION.
       PROGRAM-ID. find-mean.

       DATA DIVISION.
       LOCAL-STORAGE SECTION.
       01  i                       PIC 9(4).
       
       01  summ                    USAGE FLOAT-LONG.
       
       LINKAGE SECTION.
       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
           .</syntaxhighlight>

=={{header|Cobra}}==

<syntaxhighlight lang="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])]"
</syntaxhighlight>

Output:
<pre>
mean of [] is 0
mean of [1, 2, 3, 4] is 2.5
</pre>

=={{header|CoffeeScript}}==
<syntaxhighlight lang="coffeescript">
mean = (array) ->
 return 0 if array.length is 0
 sum = array.reduce (s,i,0) -> s += i
 sum / array.length
 
 
alert mean [1]  
</syntaxhighlight>

=={{header|Common Lisp}}==
'''With Reduce'''

<syntaxhighlight lang="lisp">(defun mean (&rest sequence)
  (when sequence
    (/ (reduce #'+ sequence) (length sequence))))</syntaxhighlight>

'''With Loop'''
<syntaxhighlight lang="lisp">(defun mean (list)
  (when list
    (/ (loop for i in list sum i)
       (length list))))</syntaxhighlight>

=={{header|Craft Basic}}==
<syntaxhighlight lang="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</syntaxhighlight>
{{out| Output}}<pre>3.83</pre>

=={{header|Crystal}}==
<syntaxhighlight lang="ruby"># Crystal will return NaN if an empty array is passed
def mean(arr) : Float64
  arr.sum / arr.size.to_f
end</syntaxhighlight>

=={{header|D}}==
===Imperative Version===
<syntaxhighlight lang="d">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);
}</syntaxhighlight>
{{out}}
<pre>mean: 0
mean: 3.83333</pre>
===More Functional Version===
<syntaxhighlight lang="d">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);
}</syntaxhighlight>
{{out}}
<pre>Mean: 0
Mean: 3.83333</pre>

===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):
<syntaxhighlight lang="d">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]));
}</syntaxhighlight>
{{out}}
<pre> 0.00000
 2.87500
 2.87500</pre>

=={{header|Dart}}==
<syntaxhighlight lang="d">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]));
}</syntaxhighlight>
{{out}}
<pre>4.0</pre>

=={{header|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. 

<syntaxhighlight lang="dc">1 2 3 5 7 zsn1k[+z1<+]ds+xln/p
3.6</syntaxhighlight>

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

<syntaxhighlight lang="dc">[[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</syntaxhighlight>

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:

<syntaxhighlight lang="dc">$ dc sample.dc amean.cd
Sum: 18
Mean: 3.6
Sample size: 5
$</syntaxhighlight>

=={{header|Delphi}}==
<syntaxhighlight lang="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.</syntaxhighlight>

=={{header|Dyalect}}==

<syntaxhighlight lang="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)</syntaxhighlight>

=={{header|E}}==

Slightly generalized to support any object that allows iteration.

<syntaxhighlight lang="e">def meanOrZero(numbers) {
    var count := 0
    var sum := 0
    for x in numbers {
        sum += x
        count += 1
    }
    return sum / 1.max(count)
}</syntaxhighlight>

=={{header|EasyLang}}==
<syntaxhighlight lang="text">
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
</syntaxhighlight>

=={{header|EchoLisp}}==
'''(mean values)''' is included in math.lib. values may be a list, vector, sequence, or any kind of procrastinator.
<syntaxhighlight lang="scheme">
(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
</syntaxhighlight>

=={{header|ECL}}==
<syntaxhighlight lang="ecl">
AveVal(SET OF INTEGER s) := AVE(s);
 
//example usage

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

=={{header|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.
<syntaxhighlight lang="edsac">
[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
    [0]   PF PF         [average value]
    [2]   PF PF         [reciprocal of data count]
    [4]   PF            [data count]
    [5]   PD            [17-bit constant 1; also serves as '0' for printing]
    [6]   @F            [carriage return]
    [7]   &F            [line feed]
    [8]   !F            [space]
    [9]   MF            [dot (in figures mode)]
   [10]   K4096F        [teleprinter null]
[Entry and outer loop]
   [11]   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)]
   [18]   A18@ G156F    [print N (clears acc)]
          TD            [clear whole of 0D, including sandwich bit]
          T4D           [same for 4D]
          A4@ S2F       [acc := N - 2]
          G66@          [jump to special action if N = 1]
          A2F           [restore N after test]
          T5F           [store N in 4D high word]
          A5@ T1F       [store 1 in 0D high word]
   [29]   A29@ G120F    [call library subroutine D6, sets 0D := 0D/4D]
          AD T2#@       [load and save 1/N]
          T#@           [clear average]
          S4@           [load -N]
[Inner loop]
   [35]   T4@           [update negative loop counter]
   [36]   A36@ G78F     [read next datum to 0D (clears acc)]
          H2#@          [mult reg := 1/N]
          VD            [acc := datum/N]
          A#@ T#@       [add into average]
          A4@ A5@       [increment negative loop counter]
          G35@          [loop until counter = 0]
   [45]   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.]
   [47]   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]
   [52]   TD            [average < 0; clear acc]
          S#@ TD        [pass abs(average) to subroutine P1]
          O47@          [print minus sign]
   [56]   O5@ O9@       [common code: print '0.']
   [58]   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]
   [64]   O10@          [print null to flush teleprinter buffer]
   [65]   ZF            [halt the machine (order also serves as plus sign)]
[Jump to here if data count = 1]
   [66]   TF            [clear acc]
   [67]   A67@ G78F     [read datum to 0D]
          AD T#@        [average := datum]
          E45@          [jump to print the average]

[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
          GKT45KP26@TZA3FTHTDT4DA6HT9@H1HS4HT6FIFAFS4HE7HT7FV4DL8FADT4DA6FA5HG8@
          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
          GKA3FT34@S4DE13@T4DSDTDE2@T4DADLDTDA4DLDE8@RDU4DLDA35@
          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+
</syntaxhighlight>
{{out}}
<pre>
     COUNT      AVERAGE
         7  -0.14285714
         1  +0.98765432
         9  +0.31666666
         9  -0.05000000
        31  +0.16774193
</pre>

=={{header|Elena}}==
ELENA 6.x:
<syntaxhighlight lang="elena">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 ",
        array.average()).readChar()
}</syntaxhighlight>
{{out}}
<pre>
Arithmetic mean of {1,2,3,4,5,6,7,8} is 4.5
</pre>

=={{header|Elixir}}==
<syntaxhighlight lang="elixir">defmodule Average do
  def mean(list), do: Enum.sum(list) / length(list)
end</syntaxhighlight>

=={{header|Emacs Lisp}}==
<syntaxhighlight lang="lisp">(defun mean (lst)
  (/ (float (apply '+ lst)) (length lst)))
(mean '(1 2 3 4))</syntaxhighlight>

{{libheader|Calc}}

<syntaxhighlight lang="lisp">(let ((x '(1 2 3 4)))
  (calc-eval "vmean($1)" nil (append '(vec) x)))</syntaxhighlight>

=={{header|EMal}}==
<syntaxhighlight lang="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))
</syntaxhighlight>
{{out}}
<pre>
0.0
3.8333333333333333333333333333
</pre>

=={{header|Erlang}}==
<syntaxhighlight lang="erlang">mean([]) -> 0;
mean(L)  -> lists:sum(L)/erlang:length(L).</syntaxhighlight>

=={{header|Euphoria}}==
<syntaxhighlight lang="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)</syntaxhighlight>

=={{header|Excel}}==
Assuming the values are entered in the A column, type into any cell which will not be part of the list:

<syntaxhighlight lang="excel">=AVERAGE(A1:A10)</syntaxhighlight>

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

<syntaxhighlight lang="excel">=AVERAGE(</syntaxhighlight>

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

<pre>
1	5,5
2	
3	
4	
5	
6	
7	
8	
9	
10	
</pre>

=={{header|F_Sharp|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 [[wp:Moving_average|wikipedia]] about the moving average computation.
<syntaxhighlight lang="fsharp">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</syntaxhighlight>

Checking this:
<syntaxhighlight lang="fsharp"> > 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</syntaxhighlight>

We can also make do with the built-in ''List.average'' function:
<syntaxhighlight lang="fsharp">List.average [4;1;7;5;8;4;5;2;1;5;2;5]</syntaxhighlight>

=={{header|Factor}}==
<syntaxhighlight lang="factor">USING: math math.statistics ;

: arithmetic-mean ( seq -- n )
    [ 0 ] [ mean ] if-empty ;</syntaxhighlight>

Tests:

<syntaxhighlight lang="factor">( scratchpad ) { 2 3 5 } arithmetic-mean >float
3.333333333333333</syntaxhighlight>

=={{header|Fantom}}==

<syntaxhighlight lang="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))
    }
  }
}
</syntaxhighlight>

=={{header|Fish}}==
<syntaxhighlight lang="fish">!vl0=?vl1=?vl&!
v<  +<>0n; >n;
>l1)?^&,n;</syntaxhighlight>
Must be called with the values pre-populated on the stack, which can be done in the <tt>fish.py</tt> interpreter with the <tt>-v</tt> switch:
<pre>fish.py mean.fish -v 10 100 47 207.4</pre>
which generates:
<pre>91.1</pre>

=={{header|Forth}}==
<syntaxhighlight lang="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</syntaxhighlight>

=={{header|Fortran}}==
In ISO Fortran 90 or later, use the SUM intrinsic, the SIZE intrinsic and the MAX intrinsic (to avoid divide by zero):
<syntaxhighlight lang="fortran">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)</syntaxhighlight>

=={{header|FreeBASIC}}==
<syntaxhighlight lang="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
</syntaxhighlight>

{{out}}
<pre>
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'
</pre>

=={{header|Frink}}==
The following works on arrays or sets.  If the collection is empty, this returns the special value <CODE>undef</CODE>.
<syntaxhighlight lang="frink">
mean[x] := length[x] > 0 ? sum[x] / length[x] : undef
</syntaxhighlight>



=={{header|FutureBasic}}==
<syntaxhighlight lang="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
</syntaxhighlight>
{{output}}
<pre>
Mean average of 10 numbers: 5.5
Man average of 6 numbers: 3.83333333333333333333333333333333333333
</pre>


</pre>

=={{header|GAP}}==
<syntaxhighlight lang="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</syntaxhighlight>

=={{header|GEORGE}}==
<syntaxhighlight lang="george">R (n) P ;
0
1, n rep (i)
   R P +
]
n div
P</syntaxhighlight>
Output:
<pre>
 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
</pre>

=={{header|GFA Basic}}==

This works for arrays of integers.

<syntaxhighlight lang="text">
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
</syntaxhighlight>

=={{header|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.

<syntaxhighlight lang="go">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")
        }
    }
}</syntaxhighlight>
{{out}}
<pre>
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
</pre>

=={{header|Groovy}}==
<syntaxhighlight lang="groovy">def avg = { list -> list == [] ? 0 : list.sum() / list.size() }</syntaxhighlight>

Test Program:
<syntaxhighlight lang="groovy">println avg(0..9)
println avg([2,2,2,4,2])
println avg ([])</syntaxhighlight>

Output:
<pre>4.5
2.4
0</pre>

=={{header|Haskell}}==
This function works if the element type is an instance of Fractional:
<syntaxhighlight lang="haskell">mean :: (Fractional a) => [a] -> a
mean [] = 0
mean xs = sum xs / Data.List.genericLength xs</syntaxhighlight>

But some types, e.g. integers, are not Fractional; the following function works for all Real types:
<syntaxhighlight lang="haskell">meanReals :: (Real a, Fractional b) => [a] -> b
meanReals = mean . map realToFrac</syntaxhighlight>

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

<syntaxhighlight lang="haskell">{-# 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]</syntaxhighlight>

=={{header|HicEst}}==
<syntaxhighlight lang="hicest">REAL :: vec(100)               ! no zero-length arrays in HicEst

   vec = $ - 1/2               ! 0.5 ... 99.5
   mean = SUM(vec) / LEN(vec)  ! 50
END </syntaxhighlight>

=={{header|Hy}}==
Returns <tt>None</tt> if the input is of length zero.
<syntaxhighlight lang="clojure">(defn arithmetic-mean [xs]
    (if xs
        (/ (sum xs) (len xs))))</syntaxhighlight>

=={{header|Icon}} and {{header|Unicon}}==
<syntaxhighlight lang="icon">procedure main(args)
    every (s := 0) +:= !args
    write((real(s)/(0 ~= *args)) | 0)
end</syntaxhighlight>

Sample outputs:
<pre>->am 1 2 3 4 5 6 7
4.0
->am
0
-></pre>

=={{header|IDL}}==

If truly only the mean is wanted, one could use

<syntaxhighlight lang="idl">x = [3,1,4,1,5,9]
print,mean(x)</syntaxhighlight>

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

<syntaxhighlight lang="idl">print,moment(x)
; ==>
  3.83333      8.96667     0.580037     -1.25081</syntaxhighlight>

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 <tt><Undefined></tt>.

=={{header|J}}==

<syntaxhighlight lang="j">mean=: +/ % #</syntaxhighlight>

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

<syntaxhighlight lang="j">   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</syntaxhighlight>

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.

<syntaxhighlight lang="j">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</syntaxhighlight>

=={{header|Java}}==
{{works with|Java|1.5+}}

<syntaxhighlight lang="java5">public static double avg(double... arr) {
    double sum = 0.0;
    for (double x : arr) {
        sum += x;
    }
    return sum / arr.length;
}</syntaxhighlight>

=={{header|JavaScript}}==

===ES5===

<syntaxhighlight lang="javascript">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</syntaxhighlight>

Using the native function `.forEach()`:
<syntaxhighlight lang="javascript">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</syntaxhighlight>

Using the native function `.reduce()`:
<syntaxhighlight lang="javascript">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
</syntaxhighlight>

Extending the `Array` prototype:
<syntaxhighlight lang="javascript">Array.prototype.mean = function() {
    return !this.length ? 0
        : this.reduce(function(pre, cur, i) {
            return (pre * i + cur) / (i + 1);
            });
    }

alert( [1,2,3,4,5].mean() );   // 3
alert( [].mean() );            // 0
</syntaxhighlight>


{{libheader|Functional}}
<syntaxhighlight lang="javascript">function mean(a)
{
 return a.length ? Functional.reduce('+', 0, a) / a.length : 0;
}</syntaxhighlight>


===ES6===

<syntaxhighlight lang="javascript">(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]);</syntaxhighlight>

{{Out}}
<syntaxhighlight lang="javascript">5</syntaxhighlight>

=={{header|Joy}}==
<syntaxhighlight lang="joy">DEFINE avg == dup 0. [+] fold swap size 1 max /.</syntaxhighlight>

=={{header|jq}}==
The mean of an array of numbers can be computed by simply writing
<syntaxhighlight lang="jq">add/length</syntaxhighlight>

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:
<syntaxhighlight lang="jq">def mean: if length == 0 then null 
  else add/length
  end;</syntaxhighlight>

=={{header|Julia}}==
Julia's built-in mean function accepts AbstractArrays (vector, matrix, etc.)
<syntaxhighlight lang="julia">julia> using Statistics; mean([1,2,3])
2.0
julia> mean(1:10)
5.5
julia> mean([])
ERROR: mean of empty collection undefined: []</syntaxhighlight>

=={{header|K}}==
<syntaxhighlight lang="k">  mean: {(+/x)%#x}
  mean 1 2 3 5 7
3.6
  mean@!0    / empty array
0.0</syntaxhighlight>

=={{header|Kotlin}}==
Kotlin has builtin functions for some collection types.
Example:
<syntaxhighlight lang="scala">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()))
}</syntaxhighlight>

=={{header|KQL}}==
<syntaxhighlight lang="kql">
let dataset = datatable(values:real)[
                        1,      1.5,    3,  5,      6.5];

dataset|summarize avg(values) 
</syntaxhighlight>

Output:
<pre>
	
avg_values
3.4
</pre>

=={{header|LabVIEW}}==
{{VI solution|LabVIEW_Averages_Arithmetic_mean.png}}

=={{header|Lambdatalk}}==
<syntaxhighlight lang="scheme">
{def mean
 {lambda {:s}
  {if {S.empty? :s}
   then 0
   else {/ {+ :s} {S.length :s}}}}}

{mean {S.serie 0 1000}}
-> 500
</syntaxhighlight>

=={{header|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.

<syntaxhighlight lang="langur">val .mean = fn(.x) { fold(fn{+}, .x) / len(.x) }

writeln "  custom: ", .mean([7, 3, 12])
writeln "built-in: ", mean([7, 3, 12])</syntaxhighlight>

{{out}}
<pre>  custom: 7.333333333333333333333333333333333
built-in: 7.333333333333333333333333333333333</pre>

=={{header|Lasso}}==
<syntaxhighlight lang="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</syntaxhighlight>

=={{header|LFE}}==

=== 1-Arity ===

<syntaxhighlight lang="lisp">
(defun mean (data)
  (/ (lists:sum data)
     (length data)))
</syntaxhighlight>

Usage:
<syntaxhighlight lang="lisp">> (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</syntaxhighlight>

=== n-Arity ===

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

<syntaxhighlight lang="lisp">(defmacro mean args
  `(/ (lists:sum ,args)
      ,(length args)))</syntaxhighlight>

Usage:

<syntaxhighlight lang="lisp">> (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</syntaxhighlight>

=={{header|Liberty BASIC}}==
<syntaxhighlight lang="lb">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
 </syntaxhighlight>

=={{header|Limbo}}==
<syntaxhighlight lang="limbo">implement Command;

include "sys.m";
sys: Sys;

include "draw.m";

include "sh.m";

init(nil: ref Draw->Context, nil: list of string)
{
	sys = load Sys Sys->PATH;

	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);
}</syntaxhighlight>

=={{header|Lingo}}==
<syntaxhighlight lang="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</syntaxhighlight>

<syntaxhighlight lang="lingo">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</syntaxhighlight>

=={{header|LiveCode}}==
Livecode provides arithmeticMean (avg, average) built-in.
<syntaxhighlight lang="livecode">average(1,2,3,4,5)  -- 3
average(empty)  -- 0</syntaxhighlight>

=={{header|Logo}}==
<syntaxhighlight lang="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</syntaxhighlight>

=={{header|Logtalk}}==
Logtalk's standard library provides an arithmetic average predicate but we ignore it here. Representing a vector using a list:
<syntaxhighlight lang="logtalk">
:- 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.
</syntaxhighlight>
Sample output:
<syntaxhighlight lang="text">
| ?- averages::arithmetic([1,2,3,4,5,6,7,8,9,10], Mean).
Mean = 5.5
yes
</syntaxhighlight>

=={{header|LSL}}==
<syntaxhighlight lang="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));
    }
}</syntaxhighlight>
Output:
<pre>
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
</pre>

=={{header|Lua}}==
<syntaxhighlight lang="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}))</syntaxhighlight>

=={{header|Lucid}}==

<syntaxhighlight lang="lucid">avg(x)
 where 
    sum = first(x) fby sum + next(x);
    n = 1 fby n + 1;
    avg = sum / n;
 end</syntaxhighlight>

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

<syntaxhighlight lang="m4">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</syntaxhighlight>
<syntaxhighlight lang="m4">mean(0,100,200,300,400,500,600,700,800,900,1000)</syntaxhighlight>

=={{header|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.
<syntaxhighlight lang="maple">
mean := proc( a :: indexable )
        local   i;
        Normalizer( add( i, i in a ) / numelems( a ) )
end proc:
</syntaxhighlight>
For example:
<syntaxhighlight lang="maple">
> 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
</syntaxhighlight>
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.
<syntaxhighlight lang="maple">mean := () -> Normalizer( `+`( args ) / nargs ):</syntaxhighlight>
This can be called as in the following examples.
<syntaxhighlight lang="maple">
> 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
</syntaxhighlight>
If desired, we can add argument type-checking as follows.
<syntaxhighlight lang="maple">mean := ( s :: seq(algebraic) ) -> Normalizer( `+`( args ) / nargs ):</syntaxhighlight>

=={{header|Mathematica}} / {{header|Wolfram Language}}==
Modify the built-in Mean function to give 0 for empty vectors (lists in Mathematica):
<syntaxhighlight lang="mathematica">Unprotect[Mean];
Mean[{}] := 0</syntaxhighlight>
Examples:
<syntaxhighlight lang="mathematica">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}]</syntaxhighlight>
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):
<syntaxhighlight lang="mathematica">4
4.53333
-1.3835
0
77/240
1/5 (-3+2 a+c+Pi)</syntaxhighlight>

=={{header|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.

<syntaxhighlight lang="text">
/*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;
</syntaxhighlight>

When run this produces:

<syntaxhighlight lang="text">
GLPSOL: GLPK LP/MIP Solver, v4.47
Parameter(s) specified in the command line:
 --nopresol --math AM.mprog
Reading model section from AM.mprog...
24 lines were read
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
</syntaxhighlight>

=={{header|MATLAB}}==
<syntaxhighlight lang="matlab">function meanValue = findmean(setOfValues)
   meanValue = mean(setOfValues);
end</syntaxhighlight>

=={{header|Maxima}}==
<syntaxhighlight lang="maxima">load("descriptive");
mean([2, 7, 11, 17]);</syntaxhighlight>

=={{header|MAXScript}}==
<syntaxhighlight lang="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))</syntaxhighlight>

=={{header|Mercury}}==
<syntaxhighlight lang="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.</syntaxhighlight>

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

<syntaxhighlight lang="mercury">:- func mean(list(float)::in(non_empty_list)) = (float::out).

mean(Ns) = foldl((+), Ns, 0.0) / float(length(Ns)).</syntaxhighlight>

=={{header|min}}==
Returns <code>nan</code> for an empty quotation.
{{works with|min|0.37.0}}
<syntaxhighlight lang="min">(2 3 5) avg puts!</syntaxhighlight>
{{out}}
<pre>3.333333333333333</pre>

=={{header|MiniScript}}==

<syntaxhighlight lang="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)</syntaxhighlight>

=={{header|МК-61/52}}==
<syntaxhighlight lang="text">0	П0	П1	С/П	ИП0	ИП1	*	+	ИП1	1
+	П1	/	П0	БП	03</syntaxhighlight>

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

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

=={{header|Modula-2}}==
<syntaxhighlight lang="modula2">PROCEDURE  Avg;

VAR     avg             : REAL;

BEGIN
   avg := sx / n;
   InOut.WriteString ("Average = ");
   InOut.WriteReal (avg, 8, 2);
   InOut.WriteLn
END Avg;</syntaxhighlight>
OR
<syntaxhighlight lang="modula2">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;</syntaxhighlight>

=={{header|MUMPS}}==
<syntaxhighlight lang="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,"^"))</syntaxhighlight>
<pre>USER>W $$MEAN^ROSETTA
No data
USER>W $$MEAN^ROSETTA("")
Empty Set
USER>
 
USER>W $$MEAN^ROSETTA("1^6^12^4")
5.75
</pre>

=={{header|Nanoquery}}==
<syntaxhighlight lang="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</syntaxhighlight>

=={{header|Nemerle}}==
<syntaxhighlight lang="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]));
    }
}</syntaxhighlight>

=={{header|NetRexx}}==
<syntaxhighlight lang="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
</syntaxhighlight>
'''Output:'''
<pre>
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...

</pre>

=={{header|NewLISP}}==
<syntaxhighlight lang="newlisp">(define (Mean Lst)
   (if (empty? Lst)
      0
      (/ (apply + Lst) (length Lst)))) 
 
 (Mean (sequence 1 1000))-> 500
 (Mean '()) -> 0</syntaxhighlight>

=={{header|Nial}}==
in the standard way, mean is
<syntaxhighlight lang="nial">mean is / [sum, tally] 

mean 6 2 4 
= 4</syntaxhighlight>
but it fails with 0 length vectors. so using a tally with a minimum value 1

<syntaxhighlight lang="nial">dtally is recur [ empty rest, 1 first, 1 first, plus, rest ]
mean is / [sum, dtally]

mean []
=0</syntaxhighlight>

=={{header|Nim}}==
{{trans|C}}
<syntaxhighlight lang="nim">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)</syntaxhighlight>
Output:
<pre>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</pre>

=={{header|Niue}}==
<syntaxhighlight lang="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
</syntaxhighlight>

=={{header|Oberon-2}}==
Oxford Oberon-2
<syntaxhighlight lang="oberon2">
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;
	RETURN total/LEN(a)
END Avg;
VAR
	ary: ARRAY MAXSIZE OF REAL;
BEGIN
	ary[0] := 10.0;
	ary[1] := 11.01;
	ary[2] := 12.02;
	ary[3] := 13.03;
	ary[4] := 14.04;
	ary[5] := 15.05;
	ary[6] := 16.06;
	ary[7] := 17.07;
	ary[8] := 18.08;
	ary[9] := 19.09;
	Out.Fixed(Avg(ary),4,2);Out.Ln
END AvgMean.
</syntaxhighlight>
Output:
<pre>
14.55
</pre>

=={{header|Objeck}}==
<syntaxhighlight lang="objeck">
function : native : PrintAverage(values : FloatVector) ~ Nil {
  values->Average()->PrintLine();
}
</syntaxhighlight>

=={{header|OCaml}}==
These functions return a float:

<syntaxhighlight lang="ocaml">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)</syntaxhighlight>

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.

<syntaxhighlight lang="ocaml">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)
;;</syntaxhighlight>

=={{header|Octave}}==

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

<syntaxhighlight lang="octave">function m = omean(l)
  if ( numel(l) == 0 )
    m = 0;
  else
    m = mean(l);
  endif
endfunction

disp(omean([]));
disp(omean([1,2,3]));</syntaxhighlight>

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

<syntaxhighlight lang="octave">function m = omean(l)
     n = sum(~isnan(l));
     l(isnan(l))=0;
     s = sum(l);
     m = s./n; 
end;</syntaxhighlight>

=={{header|Oforth}}==

<syntaxhighlight lang="oforth">: avg ( x -- avg )
   x sum 
   x size dup ifZero: [ 2drop null ] else: [ >float / ]
;</syntaxhighlight>

{{out}}
<pre>
[1, 2, 2.718, 3, 3.142] avg .
2.372 ok
[ ] avg .
null ok
</pre>

=={{header|ooRexx}}==
<syntaxhighlight lang="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
</syntaxhighlight>
Output:
<pre>
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
</pre>

=={{header|Oz}}==
A version working on floats:
<syntaxhighlight lang="oz">declare
  fun {Mean Xs}
     {FoldL Xs Number.'+' 0.0} / {Int.toFloat {Length Xs}}
  end 
in
  {Show {Mean [3. 1. 4. 1. 5. 9.]}}</syntaxhighlight>

=={{header|PARI/GP}}==
<syntaxhighlight lang="parigp">avg(v)={
  if(#v,vecsum(v)/#v)
};</syntaxhighlight>

=={{header|Pascal}}==
<syntaxhighlight lang="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.</syntaxhighlight>

Output:
<pre>
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
</pre>

Alternative version using the Math unit:

<syntaxhighlight lang="pascal">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.</syntaxhighlight>

=={{header|Perl}}==
<syntaxhighlight lang="perl">sub avg {
  @_ or return 0;
  my $sum = 0;
  $sum += $_ foreach @_;
  return $sum/@_;
}
 
print avg(qw(3 1 4 1 5 9)), "\n";</syntaxhighlight>

=={{header|Phix}}==
<!--<syntaxhighlight lang="phix">(phixonline)-->
 <span style="color: #008080;">with</span> <span style="color: #008080;">javascript_semantics</span>
 <span style="color: #008080;">function</span> <span style="color: #000000;">mean</span><span style="color: #0000FF;">(</span><span style="color: #004080;">sequence</span> <span style="color: #000000;">s</span><span style="color: #0000FF;">)</span>
     <span style="color: #008080;">if</span> <span style="color: #7060A8;">length</span><span style="color: #0000FF;">(</span><span style="color: #000000;">s</span><span style="color: #0000FF;">)=</span><span style="color: #000000;">0</span> <span style="color: #008080;">then</span> <span style="color: #008080;">return</span> <span style="color: #000000;">0</span> <span style="color: #008080;">end</span> <span style="color: #008080;">if</span>
     <span style="color: #008080;">return</span> <span style="color: #7060A8;">sum</span><span style="color: #0000FF;">(</span><span style="color: #000000;">s</span><span style="color: #0000FF;">)/</span><span style="color: #7060A8;">length</span><span style="color: #0000FF;">(</span><span style="color: #000000;">s</span><span style="color: #0000FF;">)</span>
 <span style="color: #008080;">end</span> <span style="color: #008080;">function</span>
  
 <span style="color: #0000FF;">?</span> <span style="color: #000000;">mean</span><span style="color: #0000FF;">({</span><span style="color: #000000;">1</span><span style="color: #0000FF;">,</span> <span style="color: #000000;">2</span><span style="color: #0000FF;">,</span> <span style="color: #000000;">5</span><span style="color: #0000FF;">,</span> <span style="color: #0000FF;">-</span><span style="color: #000000;">5</span><span style="color: #0000FF;">,</span> <span style="color: #0000FF;">-</span><span style="color: #000000;">9.5</span><span style="color: #0000FF;">,</span> <span style="color: #000000;">3.14159</span><span style="color: #0000FF;">})</span>
<!--</syntaxhighlight>-->

=={{header|Phixmonti}}==
<syntaxhighlight lang="phixmonti">1 2 5 -5 -9.5 3.14159 stklen tolist
len swap sum swap / print</syntaxhighlight>

=={{header|PHP}}==
<syntaxhighlight lang="php">$nums = array(3, 1, 4, 1, 5, 9);
if ($nums)
    echo array_sum($nums) / count($nums), "\n";
else
    echo "0\n";</syntaxhighlight>


=={{header|Picat}}==
<syntaxhighlight lang="picat">mean([]) = false.
mean(V) = sum(V) / len(V).</syntaxhighlight>

=={{header|PicoLisp}}==
<syntaxhighlight lang="picolisp">(de mean (Lst)
   (if (atom Lst)
      0
      (/ (apply + Lst) (length Lst)) ) )</syntaxhighlight>
Output:
<pre>: (mean (range 1 1000))
-> 500</pre>

=={{header|PL/I}}==
<syntaxhighlight lang="pli">arithmetic_mean = sum(A)/dimension(A,1);</syntaxhighlight>

=={{header|Plain English}}==
<syntaxhighlight lang="plainenglish">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.</syntaxhighlight>
{{out}}
<pre>
A list: 1, 2, 5-1/3, 7-1/2
The list's mean: 3-23/24
</pre>

=={{header|Pop11}}==

<syntaxhighlight lang="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;</syntaxhighlight>

=={{header|PostScript}}==
<syntaxhighlight lang="text">
/findmean{
/x exch def
/sum 0 def
/i 0 def
x length 0 eq
{}
{
x length{
/sum sum x i get add def
/i i 1 add def
}repeat
/sum sum x length div def
}ifelse
sum ==
}def
</syntaxhighlight>

{{libheader|initlib}}
{{works with|Ghostscript}}
<syntaxhighlight lang="postscript">
/avg {
    dup length
    {0 gt} {
       exch 0 {add} fold exch div 
    } {
        exch pop 
    } ifte
}.
</syntaxhighlight>

=={{header|PowerShell}}==
The hard way by calculating a sum and dividing:
<syntaxhighlight lang="powershell">function mean ($x) {
    if ($x.Count -eq 0) {
        return 0
    } else {
        $sum = 0
        foreach ($i in $x) {
            $sum += $i
        }
        return $sum / $x.Count
    }
}</syntaxhighlight>
or, shorter, by using the <code>Measure-Object</code> cmdlet which already knows how to compute an average:
<syntaxhighlight lang="powershell">function mean ($x) {
    if ($x.Count -eq 0) {
        return 0
    } else {
        return ($x | Measure-Object -Average).Average
    }
}</syntaxhighlight>

=={{header|Processing}}==
<syntaxhighlight lang="processing">float mean(float[] arr) {
  float out = 0;
  for (float n : arr) {
    out += n;
  }
  return out / arr.length;
}</syntaxhighlight>

=={{header|Prolog}}==

{{works with|SWI-Prolog|6.6}}

<syntaxhighlight lang="prolog">
mean(List, Mean) :-
    length(List, Length),
    sumlist(List, Sum),
    Mean is Sum / Length.
</syntaxhighlight>

=={{header|PureBasic}}==
<syntaxhighlight lang="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</syntaxhighlight>

=={{header|Python}}==
{{works with|Python|3.0}}.<br>{{works with|Python|2.6}}<br>
Uses [http://docs.python.org/3.3/library/math.html?highlight=fsum#math.fsum fsum] which tracks multiple partial sums to avoid losing precision
<syntaxhighlight lang="python">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]))</syntaxhighlight>

{{out}}
<syntaxhighlight lang="python">2.3
2.3</syntaxhighlight>


{{works with|Python|2.5}}
<syntaxhighlight lang="python">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]))</syntaxhighlight>

{{out}}
(Notice how the second call gave the wrong result)
<syntaxhighlight lang="python">2.3
1e-21</syntaxhighlight>


{{works with|Python|2.4}}
<syntaxhighlight lang="python">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])</syntaxhighlight>

{{out}}
<syntaxhighlight lang="python">2.3</syntaxhighlight>

{{works with|Python|3.4}}
Since 3.4, Python has a [[http://docs.python.org/3/library/statistics.html 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.)
<syntaxhighlight lang="python">>>> 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')</syntaxhighlight>

=={{header|Q}}==
A built-in solution is <tt>avg</tt>. An implementation of it could be:
<syntaxhighlight lang="q">mean:{(sum x)%count x}</syntaxhighlight>

=={{header|Quackery}}==

Using the Quackery big number rational arithmetic library <code>bigrat.qky</code>.

<syntaxhighlight lang="quackery">  [ $ '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  (   -->     )</syntaxhighlight>

{{out}}

<pre>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</pre>

=={{header|R}}==
R has its <tt>mean</tt> 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:

<syntaxhighlight lang="rsplus">omean <- function(v) {
  m <- mean(v)
  ifelse(is.na(m), 0, m)
}</syntaxhighlight>

=={{header|Racket}}==

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

<syntaxhighlight lang="racket">
#lang racket
(require math)

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

=={{header|Raku}}==
(formerly Perl 6)
{{works with|Rakudo|2015.10-11}}

<syntaxhighlight lang="raku" line>multi mean([]){ Failure.new('mean on empty list is not defined') }; # Failure-objects are lazy exceptions
multi mean (@a) { ([+] @a) / @a }</syntaxhighlight>

=={{header|Rapira}}==
<syntaxhighlight lang="rapira">fun mean(arr)
  sum := 0
  for N from 1 to #arr do
    sum := sum + arr[N]
  od
  return (sum / #arr)
end</syntaxhighlight>

=={{header|REBOL}}==
<syntaxhighlight lang="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]</syntaxhighlight>

Output:

<pre>[] -> 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</pre>

=={{header|Red}}==
Red comes with the <code>average</code> function.
<syntaxhighlight lang="red">Red ["Arithmetic mean"]

print average []
print average [2 3 5]</syntaxhighlight>
{{out}}
<pre>
none
3.333333333333334
</pre>

The source code for <code>average</code>:
<syntaxhighlight lang="red">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
]</syntaxhighlight>

=={{header|ReScript}}==

<syntaxhighlight lang="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))</syntaxhighlight>
{{out}}
<pre>
$ bsc arithmean.res > arithmean.js
$ node arithmean.js
5.5
</pre>

=={{header|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.
<syntaxhighlight lang="rexx">/*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. */</syntaxhighlight>
'''output''' &nbsp; when using the (internal) lists:
<pre>
       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.]
═══════════════════════════════════════════════════════════════════════════════

</pre>

=={{header|Ring}}==
<syntaxhighlight lang="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)
</syntaxhighlight>

=={{header|RPL}}==
This is based on the dc version above.
{{works with|HP|48G}}
 ≪ DUP 'N' STO →LIST ΣLIST N / 'N' PURGE ≫ '<span style="color:blue">AMEAN</span>' STO
or,by using the stack instead of a temporary variable:
 ≪ →LIST ΣLIST LASTARG SIZE / ≫  '<span style="color:blue">AMEAN</span>' STO

 CLEAR 1 2 3 5 7 DEPTH <span style="color:blue">AMEAN</span>

===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. <code>SDEV</code> and <code>VAR</code> functions.
 ≪ { 1 } OVER SIZE + RDM TRN '∑DAT' STO MEAN ≫ ''''AMEAN'''' STO

 [ 1 5 0 -4 6 ] '''AMEAN'''
{{out}}
<pre>
1: 1.6
</pre>

=={{header|Ruby}}==
<syntaxhighlight lang="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</syntaxhighlight>
{{out}}
<pre>
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
</pre>

=={{header|Run BASIC}}==
<syntaxhighlight lang="runbasic">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</syntaxhighlight>

=={{header|Rust}}==
<syntaxhighlight lang="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));
}</syntaxhighlight>
Output:
<pre>mean of [2, 3, 5, 7, 13, 21, 33, 54]: 17.25
mean of []: NaN</pre>

=={{header|Sather}}==
Built to work with VEC, ("geometric" vectors), whose elements must be floats. A 0-dimension vector yields "nan".
<syntaxhighlight lang="sather">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;</syntaxhighlight>

=={{header|Scala}}==
Using Scala 2.7, this has to be defined for each numeric type:

<syntaxhighlight lang="scala">def mean(s: Seq[Int]) = s.foldLeft(0)(_+_) / s.size</syntaxhighlight>

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

<syntaxhighlight lang="scala">def mean[T](s: Seq[T])(implicit n: Integral[T]) = {
  import n._
  s.foldLeft(zero)(_+_) / fromInt(s.size)
}</syntaxhighlight>

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

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

<syntaxhighlight lang="scala">def mean[T](s: Seq[T])(implicit n: Fractional[T]) = n.div(s.sum, n.fromInt(s.size))</syntaxhighlight>

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

=={{header|Scheme}}==
<syntaxhighlight lang="scheme">(define (mean l)
  (if (null? l)
      0
      (/ (apply + l) (length l))))</syntaxhighlight>

 > (mean (list 3 1 4 1 5 9))
 3 5/6

=={{header|Seed7}}==
<syntaxhighlight lang="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;</syntaxhighlight>

=={{header|SenseTalk}}==
SenseTalk has a built-in average function.
<syntaxhighlight lang="sensetalk">put the average of [12,92,-17,66,128]

put average(empty)
</syntaxhighlight>
{{out}}
<pre>
56.2
     nan
</pre>

=={{header|Sidef}}==
<syntaxhighlight lang="ruby">func avg(Array list) {
    list.len > 0 || return 0
    list.sum / list.len
}

say avg([Inf, 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])</syntaxhighlight>
{{out}}
<pre>
Inf
3.83333333333333333333333333333333333333333333333
2.875
3.674
-130.6625
</pre>

=={{header|Slate}}==
 
<syntaxhighlight lang="slate">[|:list| (list reduce: #+ `er ifEmpty: [0]) / (list isEmpty ifTrue: [1] ifFalse: [list size])] applyWith: #(3 1 4 1 5 9).
[|:list| (list reduce: #+ `er ifEmpty: [0]) / (list isEmpty ifTrue: [1] ifFalse: [list size])] applyWith: {}.</syntaxhighlight>

=={{header|Smalltalk}}==
<syntaxhighlight lang="smalltalk">
| numbers |

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

numbers := #(1 2 3 4 5 6 7 8).
( numbers inject: 0 into: [:sumSoFar :eachElement | sumSoFar + eachElement]) / numbers size] ) displayNl.
</syntaxhighlight>
also, most Smalltalk's collection classes already provide sum and average methods, which makes it:
{{works with|Pharo}}
{{works with|Smalltalk/X}}
<syntaxhighlight lang="smalltalk">
| numbers |

numbers := #(1 2 3 4 5 6 7 8).
(numbers sum / numbers size) displayNl.
</syntaxhighlight>
or
<syntaxhighlight lang="smalltalk">
| numbers |

numbers := #(1 2 3 4 5 6 7 8).
numbers average displayNl.
</syntaxhighlight>

=={{header|SNOBOL4}}==

{{works with|Macro Spitbol}}
{{works with|Snobol4+}}
{{works with|CSnobol}}
        
<syntaxhighlight lang="snobol4">        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</syntaxhighlight>

Output:
<pre>[1 2 3 4 5 6 7 8 9 10] -> 5.5
[ ] -> 0.</pre>

=={{header|SQL}}==
Tested on Oracle 11gR2, the more limited the tool, the more resourceful one becomes :)
<syntaxhighlight lang="sql">
create table "numbers" ("datapoint" integer);

insert into "numbers" select rownum from tab;

select sum("datapoint")/count(*)  from "numbers";
</syntaxhighlight>
...or...
<syntaxhighlight lang="sql">select avg("datapoint") from "numbers";</syntaxhighlight>

=={{header|Standard ML}}==
These functions return a real:

<syntaxhighlight lang="sml">fun mean_reals [] = 0.0
  | mean_reals xs = foldl op+ 0.0 xs / real (length xs);

val mean_ints = mean_reals o (map real);</syntaxhighlight>

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 <code>length</code> computing the length through
the <code>foldl</code>, and for mean_ints it is possible to save calling
<code>real</code> 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.

<syntaxhighlight lang="sml">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;</syntaxhighlight>

=={{header|Stata}}==
=== Mean of a dataset variable ===
Illustration of the mean on the population (in millions) in january 2016 of a few european countries (source [http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=demo_gind&lang=fr Eurostat]).
<syntaxhighlight lang="text">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</syntaxhighlight>

=== Mean in Mata ===
<syntaxhighlight lang="stata">mata
a=11311.1\7153.8\10553.8\5707.3\
82175.7\1315.9\4724.7\10783.7

mean(a)
16715.75</syntaxhighlight>

=={{header|Swift}}==
<syntaxhighlight lang="swift">func meanDoubles(s: [Double]) -> Double {
  return s.reduce(0, +) / Double(s.count)
}
func meanInts(s: [Int]) -> Double {
  return meanDoubles(s.map{Double($0)})
}</syntaxhighlight>

=={{header|Tcl}}==
<syntaxhighlight lang="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</syntaxhighlight>

=={{header|TI-83 BASIC}}==
<syntaxhighlight lang="ti83b">Mean(Ans</syntaxhighlight>

=={{header|TI-89 BASIC}}==

<syntaxhighlight lang="ti89b">Define rcmean(nums) = when(dim(nums) = 0, 0, mean(nums))</syntaxhighlight>

=={{header|Trith}}==
<syntaxhighlight lang="trith">: mean dup empty? [drop 0] [dup [+] foldl1 swap length /] branch ;

[3 1 4 1 5 9] mean</syntaxhighlight>

=={{header|TypeScript}}==
<syntaxhighlight lang="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";
}
 
alert( mean( [1,2,3,4,5] ) );   
alert( mean( [] ) );        
</syntaxhighlight>

=={{header|UNIX Shell}}==
1) First solution with bash (V >= 3), works with floats :
<syntaxhighlight lang="bash1">echo "`cat f | paste -sd+ | bc -l` / `cat f | wc -l`" | bc -l
</syntaxhighlight>
<syntaxhighlight lang="bash1">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
</syntaxhighlight>

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

<syntaxhighlight lang="bash">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</syntaxhighlight>

=={{header|UnixPipes}}==
{{incorrect|UnixPipes|There is a race between parallel commands. <code>cat count</code> might try to read the file before <code>wc -l >count</code> 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 <tt>count</tt> in the current directory.
{{works with|bash}}
<syntaxhighlight lang="bash">term() {
   b=$1;res=$2
   echo "scale=5;$res+$b" | bc
}

sum() {
  (read B; res=$1;
  test -n "$B" && (term $B $res) || (term 0 $res))
}

fold() {
  func=$1
  (while read a ; do
      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</syntaxhighlight>

=={{header|Ursa}}==
<syntaxhighlight lang="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</syntaxhighlight>

=={{header|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.
<syntaxhighlight lang="ursala">#import nat
#import flo

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

#cast %e

example = mean <5.,3.,-2.,6.,-4.></syntaxhighlight>
output:
<pre>1.600000e+00</pre>

=={{header|V}}==
<syntaxhighlight lang="v">[mean
   [sum 0 [+] fold].
   dup sum
   swap size [[1 <] [1]] when /
].</syntaxhighlight>

=={{header|Vala}}==
Using array to hold the numbers of the list:
<syntaxhighlight lang="vala">
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());
}
</syntaxhighlight>

Output:
<pre>
2.6069316666666666
0
</pre>

=={{header|VBA}}==
<syntaxhighlight lang="vb">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</syntaxhighlight>{{out}}
<pre>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[1] =  0 
mean[] = Fout 2007</pre>

=={{header|VBScript}}==
<syntaxhighlight lang="vb">
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))
</syntaxhighlight>

{{Out}}
<pre>3.83333333333333</pre>

=={{header|Vedit macro language}}==
The numeric data is stored in current edit buffer as ASCII strings, one value per line.
<syntaxhighlight lang="vedit">#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)</syntaxhighlight>

=={{header|Vim Script}}==
Throws an exception if the list is empty.
<syntaxhighlight lang="vim">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</syntaxhighlight>

=={{header|V (Vlang)}}==
<syntaxhighlight lang="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")
    }
}</syntaxhighlight>
{{out}}
<pre>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</pre>

=={{header|Wart}}==
<syntaxhighlight lang="python">def (mean l)
  sum.l / len.l</syntaxhighlight>

Example run:
<pre>mean '(1 2 3)
=> 2</pre>

=={{header|WDTE}}==
<syntaxhighlight lang="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));</syntaxhighlight>

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:
<syntaxhighlight lang="wdte">mean [1; 2; 3] -- io.writeln io.stdout;</syntaxhighlight>

Output:
<pre>2</pre>

=={{header|Wortel}}==
<syntaxhighlight lang="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]
  ]]
}</syntaxhighlight>
Returns:
<pre>[3.5714285714285716 3.5714285714285716]</pre>

=={{header|Wren}}==
<syntaxhighlight lang="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</syntaxhighlight>

=={{header|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 <tt>nil</tt>, equivalent to the empty list or logical <tt>false</tt>.
<syntaxhighlight lang="lisp">(defun mean (v)
    (if (= (vector-length v) 0)
        nil
        (let ((l (vector->list v)))
            (/ (apply + l) (length l)))))</syntaxhighlight>

=={{header|XPL0}}==
<syntaxhighlight lang="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);
]</syntaxhighlight>

Output:
<pre>
    2.60693
</pre>

=={{header|XSLT}}==

Where <code>$values</code> is some variable indicating a set of nodes containing numbers, the average is given by the XPath expression:

<syntaxhighlight lang="xpath">sum($values) div count($values)</syntaxhighlight>

===Runnable example===

<syntaxhighlight lang="xml"><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></syntaxhighlight>

Sample input:

<syntaxhighlight lang="xml"><numbers>
	<!-- Average is 2.4 -->
	<number>1</number>
	<number>1</number>
	<number>2</number>
	<number>3</number>
	<number>5</number>
</numbers></syntaxhighlight>

=={{header|Yorick}}==
<syntaxhighlight lang="yorick">func mean(x) {
    if(is_void(x)) return 0;
    return x(*)(avg);
}</syntaxhighlight>

=={{header|zkl}}==
Converts int to floats (implicitly):
<syntaxhighlight lang="zkl">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))</syntaxhighlight>
To pass in a vector/list:
<syntaxhighlight lang="zkl">fcn meanV(z){ z.reduce('+,0.0)/z.len() }
meanV(T(3,1,4,1,5,9)); // --> 3.83333</syntaxhighlight>

=={{header|Zoea}}==
<syntaxhighlight lang="zoea">
program: average
  case: 1
    input: [2,3,10]
    output: 5
  case: 2
    input: [7,11]
    output: 9   
</syntaxhighlight>

=={{header|zonnon}}==
<syntaxhighlight lang="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.
</syntaxhighlight>
{{out}}
<pre>
arithmetic mean:       13,9
</pre>