median

Median value of array

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Syntax

M = median(A)

M = median(A,"all")

M = median(A,dim)

M = median(A,vecdim)

M = median(___,missingflag)

M = median(___,Weights=W)

Description

M = median(A) returns the median value of A.

If A is a vector, then median(A) returns the median value of A.
If A is a nonempty matrix, then median(A) treats the columns of A as vectors and returns a row vector of median values.
If A is an empty 0-by-0 matrix, median(A) returns NaN.
If A is a multidimensional array, then median(A) treats the values along the first array dimension whose size does not equal 1 as vectors. The size of M in this dimension becomes 1, while the sizes of all other dimensions remain the same as in A.
If A is a table or timetable, then median(A) returns a one-row table containing the median of each variable. (since R2023a)

median returns natively in the class of A, such that class(M) = class(A).

example

M = median(A,"all") returns the median over all elements of A.

example

M = median(A,dim) returns the median of elements along dimension dim. For example, if A is a matrix, then median(A,2) returns a column vector containing the median value of each row.

example

M = median(A,vecdim) returns the median based on the dimensions specified in the vector vecdim. For example, if A is a matrix, then median(A,[1 2]) returns the median of all elements in A because every element of a matrix is contained in the array slice defined by dimensions 1 and 2.

example

M = median(___,missingflag) specifies whether to include or omit missing values in A for any of the previous syntaxes. For example, median(A,"omitmissing") ignores all missing values when computing the median. By default, median includes missing values.

example

M = median(___,Weights=W) specifies a weighting scheme W and returns the weighted median. (since R2024a)

example

Examples

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Median of Matrix Columns

Open Live Script

Define a 4-by-3 matrix.

A = [0 1 1; 2 3 2; 1 3 2; 4 2 2]

A = 4×3

     0     1     1
     2     3     2
     1     3     2
     4     2     2

Find the median value of each column.

M = median(A)

M = 1×3

    1.5000    2.5000    2.0000

For each column, the median value is the mean of the middle two numbers in sorted order.

Median of Matrix Rows

Open Live Script

Define a 2-by-3 matrix.

A = [0 1 1; 2 3 2]

A = 2×3

     0     1     1
     2     3     2

Find the median value of each row.

M = median(A,2)

For each row, the median value is the middle number in sorted order.

Median of 3-D Array

Open Live Script

Create a 1-by-3-by-4 array of integers between 1 and 10.

rng('default')
A = randi(10,[1,3,4])

A = 
A(:,:,1) =

     9    10     2


A(:,:,2) =

    10     7     1


A(:,:,3) =

     3     6    10


A(:,:,4) =

    10     2    10

Find the median values of this 3-D array along the second dimension.

M = median(A)

M = 
M(:,:,1) =

     9


M(:,:,2) =

     7


M(:,:,3) =

     6


M(:,:,4) =

    10

This operation produces a 1-by-1-by-4 array by computing the median of the three values along the second dimension. The size of the second dimension is reduced to 1.

Compute the median along the first dimension of A.

M = median(A,1);
isequal(A,M)

ans = logical
   1

This command returns the same array as A because the size of the first dimension is 1.

Median of Array Page

Open Live Script

Create a 3-D array and compute the median over each page of data (rows and columns).

A(:,:,1) = [2 4; -2 1];
A(:,:,2) = [6 2; -5 3];
A(:,:,3) = [4 4; 7 -3];
M1 = median(A,[1 2])

M1 = 
M1(:,:,1) =

    1.5000


M1(:,:,2) =

    2.5000


M1(:,:,3) =

     4

To compute the median over all dimensions of an array, you can either specify each dimension in the vector dimension argument, or use the "all" option.

M2 = median(A,[1 2 3])

M2 = 
2.5000

Mall = median(A,"all")

Mall = 
2.5000

Median of 8-Bit Integer Array

Open Live Script

Define a 1-by-4 vector of 8-bit integers.

A = int8(1:4)

A = 1x4 int8 row vector

   1   2   3   4

Compute the median value.

M = median(A)

M = int8

3

class(M)

ans = 
'int8'

M is the mean of the middle two numbers in sorted order returned as an 8-bit integer.

Median Excluding Missing Values

Open Live Script

Create a matrix containing NaN values.

A = [1.77 -0.005 NaN -2.95; NaN 0.34 NaN 0.19]

A = 2×4

    1.7700   -0.0050       NaN   -2.9500
       NaN    0.3400       NaN    0.1900

Compute the median values of the matrix, excluding missing values. For matrix columns that contain any NaN value, the median is computed over non-NaN elements. For matrix columns that contain all NaN values, the median is NaN.

M = median(A,"omitmissing")

M = 1×4

    1.7700    0.1675       NaN   -1.3800

Specify Weighting Scheme

Since R2024a

Open Live Script

Create a matrix and compute the weighted median of the matrix according to a weighting scheme specified by W. The median function applies the weighting scheme to each column in A.

A = [1 1; 7 9; 1 9; 1 9; 6 2];
W = [1 2 1 2 3]';
M = median(A,Weights=W)

M = 1×2

     6     9

For each column, the median value is the mean of the middle two numbers in sorted order.

Input Arguments

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`A` — Input data
vector | matrix | multidimensional array | table | timetable

Input data, specified as a vector, matrix, multidimensional array, table, or timetable.

`dim` — Dimension to operate along
positive integer scalar

Dimension to operate along, specified as a positive integer scalar. If you do not specify the dimension, then the default is the first array dimension whose size does not equal 1.

Dimension dim indicates the dimension whose length reduces to 1. The size(M,dim) is 1, while the sizes of all other dimensions remain the same.

Consider an m-by-n input matrix, A:

median(A,1) computes the median of the elements in each column of A and returns a 1-by-n row vector.
median(A,2) computes the median of the elements in each row of A and returns an m-by-1 column vector.

median returns A when dim is greater than ndims(A).

`vecdim` — Vector of dimensions
vector of positive integers

Vector of dimensions, specified as a vector of positive integers. Each element represents a dimension of the input array. The lengths of the output in the specified operating dimensions are 1, while the others remain the same.

Consider a 2-by-3-by-3 input array, A. Then median(A,[1 2]) returns a 1-by-1-by-3 array whose elements are the medians of each page of A.

Mapping of a 2-by-3-by-3 input array to a 1-by-1-by-3 output array

`missingflag` — Missing value condition
`"includemissing"` (default) | `"includenan"` | `"includenat"` | `"includeundefined"` | `"omitmissing"` | `"omitnan"` | `"omitnat"` | `"omitundefined"`

Missing value condition, specified as one of the values in this table.

Value	Input Data Type	Description
`"includemissing"`	All supported data types	Include missing values in `A` when computing the median. If any element in the operating dimension is missing, then the corresponding element in `M` is missing.
`"includenan"`	`double`, `single`, `duration`
`"includenat"`	`datetime`
`"includeundefined"`	`categorical`
`"omitmissing"`	All supported data types	Ignore missing values in `A`, and compute the median over fewer points. If all elements in the operating dimension are missing, then the corresponding element in `M` is missing.
`"omitnan"`	`double`, `single`, `duration`
`"omitnat"`	`datetime`
`"omitundefined"`	`categorical`

`W` — Weighting scheme
vector | matrix | multidimensional array

Since R2024a

Weighting scheme, specified as a vector, matrix, or multidimensional array. The elements of W must be nonnegative.

If you specify a weighting scheme, median returns the weighted median, which is the value in A associated with a cumulative 50% of the weights specified by W [1]. The weighted median is less affected by extreme values compared to the standard median.

If W is a vector, it must have the same length as the operating dimension. Otherwise, W must have the same size as the input data.

If the input data A is a table or timetable, then W must be a vector.

You cannot specify this argument if you specify vecdim or "all".

Data Types: double | single

Algorithms

For ordinal categorical arrays, MATLAB^® interprets the median of an even number of elements as follows:

If the number of categories between the middle two values is ...	Then the median is ...
zero (values are from consecutive categories)	larger of the two middle values
an odd number	value from category occurring midway between the two middle values
an even number	value from larger of the two categories occurring midway between the two middle values

References

[1] “Weighted median.” In Wikipedia, May 21 2023. https://en.wikipedia.org/wiki/Weighted_median.

Extended Capabilities

Tall Arrays
Calculate with arrays that have more rows than fit in memory.

The median function supports tall arrays with the following usage notes and limitations:

Input A must be a column vector to compute median in the first dimension.
The Weights name-value argument is not supported.

For more information, see Tall Arrays.

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

Usage notes and limitations:

Thread-Based Environment
Run code in the background using MATLAB® `backgroundPool` or accelerate code with Parallel Computing Toolbox™ `ThreadPool`.

This function fully supports thread-based environments. For more information, see Run MATLAB Functions in Thread-Based Environment.

GPU Arrays
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

The median function fully supports GPU arrays. To run the function on a GPU, specify the input data as a gpuArray (Parallel Computing Toolbox). For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

Distributed Arrays
Partition large arrays across the combined memory of your cluster using Parallel Computing Toolbox™.

This function fully supports distributed arrays. For more information, see Run MATLAB Functions with Distributed Arrays (Parallel Computing Toolbox).

Version History

Introduced before R2006a

expand all

R2024b: Compute weighted median for `datetime` and ordinal categorical data types

You can compute the weighted median for input data having the datetime and ordinal categorical data types. Before R2024b, you could compute only the unweighted median for these data types.

R2024b: Improved performance when computing weighted median

The median function has improved performance when you specify a weighting scheme.

For example, this code computes the weighted median of a 600-by-10 matrix. The code is about 1.8x faster than in the previous release.

function timingTest
A = rand(600,10);
W = rand(600,1);

for i = 1:1:3e2
    median(A,Weights=W); 
end
end

The approximate execution times are:

R2024a: 1.00 s

R2024b: 0.55 s

The code was timed on a Windows^® 11, AMD EPYC 74F3 24-Core Processor @ 3.19 GHz test system using the timeit function.

timeit(@timingTest)

R2024a: Compute weighted median

Compute the weighted median by specifying the Weights parameter as the weighting scheme. You can compute the weighted median for input data having numeric, logical, and duration data types.

R2023a: Specify missing value condition

Include or omit all missing values in the input array when computing the median by using the "includemissing" or "omitmissing" options. Previously, "includenan", "omitnan", "includenat", "omitnat", "includeundefined", and "omitundefined" specified a missing value condition that was specific to the data type of the input array.

R2023a: Perform calculations directly on tables and timetables

The median function can calculate on all variables within a table or timetable without indexing to access those variables. All variables must have data types that support the calculation. For more information, see Direct Calculations on Tables and Timetables.

R2018b: Operate on multiple dimensions

Operate on multiple dimensions of the input array at a time. Specify a vector of operating dimensions, or specify the "all" option to operate on all array dimensions.

median

Syntax

Description

Examples

Median of Matrix Columns

Median of Matrix Rows

Median of 3-D Array

Median of Array Page

Median of 8-Bit Integer Array

Median Excluding Missing Values

Specify Weighting Scheme

Input Arguments

A — Input data vector | matrix | multidimensional array | table | timetable

dim — Dimension to operate along positive integer scalar

vecdim — Vector of dimensions vector of positive integers

missingflag — Missing value condition "includemissing" (default) | "includenan" | "includenat" | "includeundefined" | "omitmissing" | "omitnan" | "omitnat" | "omitundefined"

W — Weighting scheme vector | matrix | multidimensional array

Algorithms

References

Extended Capabilities

Tall Arrays Calculate with arrays that have more rows than fit in memory.

C/C++ Code Generation Generate C and C++ code using MATLAB® Coder™.

Thread-Based Environment Run code in the background using MATLAB® backgroundPool or accelerate code with Parallel Computing Toolbox™ ThreadPool.

GPU Arrays Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

Distributed Arrays Partition large arrays across the combined memory of your cluster using Parallel Computing Toolbox™.

Version History

R2024b: Compute weighted median for datetime and ordinal categorical data types

R2024b: Improved performance when computing weighted median

R2024a: Compute weighted median

R2023a: Specify missing value condition

R2023a: Perform calculations directly on tables and timetables

R2018b: Operate on multiple dimensions

See Also

`A` — Input data
vector | matrix | multidimensional array | table | timetable

`dim` — Dimension to operate along
positive integer scalar

`vecdim` — Vector of dimensions
vector of positive integers

`missingflag` — Missing value condition
`"includemissing"` (default) | `"includenan"` | `"includenat"` | `"includeundefined"` | `"omitmissing"` | `"omitnan"` | `"omitnat"` | `"omitundefined"`

`W` — Weighting scheme
vector | matrix | multidimensional array

Tall Arrays
Calculate with arrays that have more rows than fit in memory.

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

Thread-Based Environment
Run code in the background using MATLAB® `backgroundPool` or accelerate code with Parallel Computing Toolbox™ `ThreadPool`.

GPU Arrays
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

Distributed Arrays
Partition large arrays across the combined memory of your cluster using Parallel Computing Toolbox™.

R2024b: Compute weighted median for `datetime` and ordinal categorical data types