{{task|Probability and statistics}}

{{task heading}}

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}}

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/%

{{out}}

0
D

11l

{{trans|Python}}

F average(x)
   R sum(x) / Float(x.len)

print(average([0, 0, 3, 1, 4, 1, 5, 9, 0, 0]))

{{out}}

2.3

360 Assembly

Compact and functional.

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

{{out}}

         5.50

6502 Assembly

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

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


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

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

			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

8th

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

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

Output is:

2.54395

NaN

10.00000

ACL2

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

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

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;
}

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.

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;

Output: 3.83333

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

Aime

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

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

    sum / ~l;
}

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

    0;
}

ALGOL 68

{{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.}}

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))
)

ALGOL W

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

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

AmigaE

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

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

PROC main()
  DEF s[20] : STRING
  WriteF('mean \s\n',
         RealF(s,mean([1.0, 2.0, 3.0, 4.0, 5.0]), 2))
ENDPROC

Applesoft BASIC

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

AntLang

AntLang has a built-in avg function.

avg[list]

Arturo

arr #(1 2 3 4 5 6 7)

print $(avg arr)

{{out}}

4

Astro

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

AutoHotkey

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

AWK

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

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

BEGIN {
  # fill a vector with random numbers
  for(i=0; i < 10; i++) {
    vett[i] = rand()*10
  }
  print mean(vett)
  print mean(nothing)
}

{{out}}

$ awk -f mean.awk
3.92689
zero-length input !

APL

{{works with|APL2}}

      X←3 1 4 1 5 9
      (+/X)÷⍴X
3.833333333

Babel

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

{{Out}}

535

BASIC

{{works with|QBasic}}

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

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

=

BBC BASIC

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

To calculate the mean of an array:

      REM specific functions for the array/vector types

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

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

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

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

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

[[User:MichaelHutton|Michael Hutton]] 14:02, 29 May 2011 (UTC)

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

## bc

Uses the current scale for calculating the mean.

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

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

Befunge

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

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

blz

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

Bracmat

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

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

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

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

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

Brat

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

p mean 1.to 10  #Prints 5.5

Burlesque

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

C

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

#include <stdio.h>

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

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

	return 0;
}

{{out}}

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

C#

using System;
using System.Linq;

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

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

using System;

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

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

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

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

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

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

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

C++

{{libheader|STL}}

#include <vector>

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

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

Shorter (and more idiomatic) version:

#include <vector>
#include <algorithm>

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

Idiomatic version templated on any kind of iterator:

#include <iterator>
#include <algorithm>

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

Chef

Mean.

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

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

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

Serves 1.

Clojure

Returns a [http://clojure.org/data_structures ratio]:

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

Returns a float:

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

COBOL

Intrinsic function:

FUNCTION MEAN(some-table (ALL))

Sample implementation:

       IDENTIFICATION DIVISION.
       PROGRAM-ID. find-mean.

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

       01  summ                    USAGE FLOAT-LONG.

       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
           .

Cobra

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

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

Output:

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

CoffeeScript

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


alert mean [1]

Common Lisp

'''With Reduce'''

(defun mean (&rest sequence)
  (if (null sequence)
      nil
      (/ (reduce #'+ sequence) (length sequence))))

'''With Loop'''

(defun mean (list)
  (unless (null list)
    (/ (loop for i in list sum i)
       (length list))))

Crystal

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

D

Imperative Version

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

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

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

void main() {
    import std.stdio;

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

{{out}}

mean: 0
mean: 3.83333

More Functional Version

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

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

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

{{out}}

Mean: 0
Mean: 3.83333

More Precise Version

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

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

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

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

{{out}}

 0.00000
 2.87500
 2.87500

Dart

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

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

{{out}}

4.0

dc

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

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

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

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

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

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

Delphi

program AveragesArithmeticMean;

{$APPTYPE CONSOLE}

uses Types;

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

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

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

Dyalect

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

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

E

Slightly generalized to support any object that allows iteration.

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

EasyLang

floatvars func mean . f[] r . for i% range len f[] s += f[i%] . r = s / len f[] . f[] = [ 1 2 3 4 5 6 7 8 ] call mean f[] r print r

## EchoLisp

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

```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

ECL

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

//example usage

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

Elena

ELENA 4.1:

import extensions;

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

        var enumerator := self.enumerator();

        while (enumerator.next())
        {
            sum += enumerator.get();
            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()
}

Elixir

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

Emacs Lisp

  (defun mean (lst)
    (/ (float (apply '+ lst)) (length lst)))
  (mean '(1 2 3 4))

Calculate mean by Emacs Lisp and built-in Emacs Calc

(setq x '[1 2 3 4])
(string-to-number (calc-eval (format "vmean(%s)" x)))

Erlang

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

Euphoria

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

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

Excel

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

=AVERAGE(A1:A10)

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

=AVERAGE(

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

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

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

Factor

USING: math math.statistics ;

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

Tests:

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

Fantom

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

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

Fish

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

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

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

which generates:

91.1

Forth

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

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

Fortran

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

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

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

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

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

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

FreeBASIC

' FB 1.05.0 Win64

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

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

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

{{out}}

Sample input and output

How many numbers are to be input ? : 6

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

Mean is 13.6

After clearing the vector, the mean is 'NaN'

Frink

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

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

=={{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.

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

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

Checking this:

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

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

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

GAP

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

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

GEORGE

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

Output:

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

GFA Basic

This works for arrays of integers.

## 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.


```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")
        }
    }
}

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

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

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

4.5
2.4
0

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

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

{-# 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]

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

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

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

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

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

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

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

mean=: +/ % #

   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

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

public static double avg(double... arr) {
    double sum = 0.0;
    for (double x : arr) {
        sum += x;
    }
    return sum / arr.length;
}

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

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

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

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

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

(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]);

## jq

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

```jq>add/length</lang


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:

```jq
def mean: if length == 0 then null
  else add/length
  end;

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

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

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()))
}

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

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

 (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

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

 (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

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

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);
}

-- v can be (2D) point, (3D) vector or list of integers/floats
on mean (v)
    case ilk(v) of
        #point: cnt = 2
        #vector: cnt = 3
        #list: cnt = v.count
        otherwise: return
    end case
    sum = 0
    repeat with i = 1 to cnt
        sum = sum + v[i]
    end repeat
    return float(sum)/cnt
end

put mean(point(1, 2.5))
-- 1.7500
put mean(vector(1.2, 4.7, 5.6))
-- 3.8333
put mean([6,12,18,24,30,36,42,48,54,60,66,72,78])
-- 42.0000

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

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

:- 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.

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

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));
    }
}

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

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}))

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

define(`extractdec', `ifelse(eval(`$1%100 < 10'),1,`0',`')eval($1%100)')dnl
define(`fmean', `eval(`($2/$1)/100').extractdec(eval(`$2/$1'))')dnl
define(`mean', `rmean(`$#', $@)')dnl
define(`rmean', `ifelse(`$3', `', `fmean($1,$2)',dnl
`rmean($1, eval($2+$3), shift(shift(shift($@))))')')dnl

mean(0,100,200,300,400,500,600,700,800,900,1000)

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

> 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

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

> 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

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

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

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}]

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

When run this produces:

<lang>
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

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

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

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))

:- 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.

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

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

(((0 (+) reduce) (size /)) cleave) :mean
(2 3 5) mean print

3.333333333333334

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)

''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}}==

```modula2
PROCEDURE  Avg;

VAR     avg             : REAL;

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

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;

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

USER>W $$MEAN^ROSETTA
No data
USER>W $$MEAN^ROSETTA("")
Empty Set
USER>

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

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

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

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

/* NetRexx */
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

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...

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

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

mean is / [sum, tally]

mean 6 2 4
= 4

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

mean []
=0

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)

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

[ [ , 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

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.

14.55

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

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)

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)
;;

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

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

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

: avg ( x -- avg )
   x sum
   x size dup ifZero: [ 2drop null ] else: [ >float / ]
;

[1, 2, 2.718, 3, 3.142] avg .
2.372 ok
[ ] avg .
null ok

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

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

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

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

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.

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

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.

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

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

multi mean([]){ Failure.new('mean on empty list is not defined') }; # Failure-objects are lazy exceptions
multi mean (@a) { ([+] @a) / @a }

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

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

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

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

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

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

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;

{{libheader|initlib}}
{{works with|Ghostscript}}

```postscript

/avg {
    dup length
    {0 gt} {
       exch 0 {add} fold exch div
    } {
        exch pop
    } ifte
}.

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

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

mean(List, Mean) :-
    length(List, Length),
    sumlist(List, Sum),
    Mean is Sum / Length.

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

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]))

2.3
2.3

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]))

2.3
1e-21

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])

{{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.)

```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')

mean:{(sum x)%count x}

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

#lang racket
(require math)

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

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]

[] -> 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

/*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. */

       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.]
═══════════════════════════════════════════════════════════════════════════════

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)

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

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

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

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

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));
}

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

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

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

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

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

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

(define (mean l)
  (if (null? l)
      0
      (/ (apply + l) (length l))))

$ 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;

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

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

inf
3.833333333333333333333333333333333333333
2.875
3.674
-130.6625

[|: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: {}.

| 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.

| numbers |

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

| numbers |

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

| numbers |

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

        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

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

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

insert into "numbers" select rownum from tab;

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

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

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

val mean_ints = mean_reals o (map real);

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;

         |       Mean   Std. Err.     [95% Conf. Interval]

###  Mean in Mata


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

mean(a)
16715.75

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

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

Mean(Ans

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

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

[3 1 4 1 5 9] mean

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( [] ) );

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

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

#
# 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

#import nat
#import flo

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

#cast %e

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

1.600000e+00

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

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());
}

2.6069316666666666
0

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

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

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))

3.83333333333333

#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)

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

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

mean '(1 2 3)
=> 2

 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));

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

2

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

@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]
  ]]
}

[3.5714285714285716 3.5714285714285716]

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

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);
]

    2.60693

sum($values) div count($values)

<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>

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

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

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))

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

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.

arithmetic mean:       13,9