Percentiles of a data set

returns percentiles of the elements in a data vector or array `Y`

= prctile(`X`

,`p`

)`X`

for the
percentages `p`

in the interval [0,100].

If

`X`

is a vector, then`Y`

is a scalar or a vector with the same length as the number of percentiles requested (`length(p)`

).`Y(i)`

contains the`p(i)`

percentile.If

`X`

is a matrix, then`Y`

is a row vector or a matrix, where the number of rows of`Y`

is equal to the number of percentiles requested (`length(p)`

). The`i`

th row of`Y`

contains the`p(i)`

percentiles of each column of`X`

.For multidimensional arrays,

`prctile`

operates along the first nonsingleton dimension of`X`

.

returns percentiles over the dimensions specified in the vector `Y`

= prctile(`X`

,`p`

,`vecdim`

)`vecdim`

.
For example, if `X`

is a matrix, then ```
prctile(X,50,[1
2])
```

returns the 50th percentile of all the elements of `X`

because every element of a matrix is contained in the array slice defined by dimensions 1
and 2.

Generate a data set of size 10.

rng('default'); % for reproducibility x = normrnd(5,2,1,10)

`x = `*1×10*
6.0753 8.6678 0.4823 6.7243 5.6375 2.3846 4.1328 5.6852 12.1568 10.5389

Calculate the 42nd percentile.

Y = prctile(x,42)

Y = 5.6709

Find the percentiles of all the values in an array.

Create a 3-by-5-by-2 array `X`

.

X = reshape(1:30,[3 5 2])

X = X(:,:,1) = 1 4 7 10 13 2 5 8 11 14 3 6 9 12 15 X(:,:,2) = 16 19 22 25 28 17 20 23 26 29 18 21 24 27 30

Find the 40th and 60th percentiles of the elements of `X`

.

`Y = prctile(X,[40 60],'all')`

`Y = `*2×1*
12.5000
18.5000

`Y(1)`

is the 40th percentile of `X`

, and `Y(2)`

is the 60th percentile of `X`

.

Calculate the percentiles along the columns and rows of a data matrix for specified percentages.

Generate a 5-by-5 data matrix.

X = (1:5)'*(2:6)

`X = `*5×5*
2 3 4 5 6
4 6 8 10 12
6 9 12 15 18
8 12 16 20 24
10 15 20 25 30

Calculate the 25th, 50th, and 75th percentiles along the columns of `X`

.

Y = prctile(X,[25 50 75],1)

`Y = `*3×5*
3.5000 5.2500 7.0000 8.7500 10.5000
6.0000 9.0000 12.0000 15.0000 18.0000
8.5000 12.7500 17.0000 21.2500 25.5000

The rows of `Y`

correspond to the percentiles of columns of `X`

. For example, the 25th, 50th, and 75th percentiles of the third column of `X`

with elements (4, 8, 12, 16, 20) are 7, 12, and 17, respectively. `Y = prctile(X,[25 50 75])`

returns the same percentile matrix.

Calculate the 25th, 50th, and 75th percentiles along the rows of `X`

.

Y = prctile(X,[25 50 75],2)

`Y = `*5×3*
2.7500 4.0000 5.2500
5.5000 8.0000 10.5000
8.2500 12.0000 15.7500
11.0000 16.0000 21.0000
13.7500 20.0000 26.2500

The rows of `Y`

correspond to the percentiles of rows of `X`

. For example, the 25th, 50th, and 75th percentiles of the first row of `X`

with elements (2, 3, 4, 5, 6) are 2.75, 4, and 5.25, respectively.

Find the percentiles of a multidimensional array along multiple dimensions simultaneously.

Create a 3-by-5-by-2 array `X`

.

X = reshape(1:30,[3 5 2])

X = X(:,:,1) = 1 4 7 10 13 2 5 8 11 14 3 6 9 12 15 X(:,:,2) = 16 19 22 25 28 17 20 23 26 29 18 21 24 27 30

Calculate the 40th and 60th percentiles for each page of `X`

by specifying dimensions 1 and 2 as the operating dimensions.

Ypage = prctile(X,[40 60],[1 2])

Ypage = Ypage(:,:,1) = 6.5000 9.5000 Ypage(:,:,2) = 21.5000 24.5000

For example, `Ypage(1,1,1)`

is the 40th percentile of the first page of `X`

, and `Ypage(2,1,1)`

is the 60th percentile of the first page of `X`

.

Calculate the 40th and 60th percentiles of the elements in each `X(:,i,:)`

slice by specifying dimensions 1 and 3 as the operating dimensions.

Ycol = prctile(X,[40 60],[1 3])

`Ycol = `*2×5*
2.9000 5.9000 8.9000 11.9000 14.9000
16.1000 19.1000 22.1000 25.1000 28.1000

For example, `Ycol(1,4)`

is the 40th percentile of the elements in `X(:,4,:)`

, and `Ycol(2,4)`

is the 60th percentile of the elements in `X(:,4,:)`

.

Calculate exact and approximate percentiles of a tall column vector for a given percentage.

When you perform calculations on tall arrays, MATLAB® uses either a parallel pool (default if you have Parallel Computing Toolbox™) or the local MATLAB session. If you want to run the example using the local MATLAB session when you have Parallel Computing Toolbox, you can change the global execution environment by using the `mapreducer`

function.

Create a datastore for the `airlinesmall`

data set. Treat `'NA'`

values as missing data so that `datastore`

replaces them with `NaN`

values. Specify to work with the `ArrTime`

variable.

ds = datastore('airlinesmall.csv','TreatAsMissing','NA',... 'SelectedVariableNames','ArrTime');

Create a tall table on top of the datastore, and extract the data from the tall table into a tall vector.

`t = tall(ds) % Tall table`

Starting parallel pool (parpool) using the 'local' profile ... Connected to the parallel pool (number of workers: 4). t = Mx1 tall table ArrTime _______ 735 1124 2218 1431 746 1547 1052 1134 : :

`x = t{:,:} % Tall vector`

x = Mx1 tall double column vector 735 1124 2218 1431 746 1547 1052 1134 : :

Calculate the exact 50th percentile of `x`

. Because `x`

is a tall column vector and `p`

is a scalar, `prctile`

returns the exact percentile value by default.

p = 50; yExact = prctile(x,p)

yExact = tall double ?

Calculate the approximate 50th percentile of x. Specify `'Method','approximate'`

to use an approximation algorithm based on T-Digest for computing the percentile.

yApprox = prctile(x,p,'Method','approximate')

yApprox = MxNx... tall double array ? ? ? ... ? ? ? ... ? ? ? ... : : : : : :

Evaluate the tall arrays and bring the results into memory by using `gather`

.

[yExact,yApprox] = gather(yExact,yApprox)

Evaluating tall expression using the Parallel Pool 'local': - Pass 1 of 4: Completed in 5.1 sec - Pass 2 of 4: Completed in 1 sec - Pass 3 of 4: Completed in 1.8 sec - Pass 4 of 4: Completed in 1.8 sec Evaluation completed in 13 sec

yExact = 1522

yApprox = 1.5220e+03

The values of the approximate percentile and the exact percentile are the same to the four digits shown.

Calculate exact and approximate percentiles of a tall matrix for specified percentages along different dimensions.

When you perform calculations on tall arrays, MATLAB® uses either a parallel pool (default if you have Parallel Computing Toolbox™) or the local MATLAB session. If you want to run the example using the local MATLAB session when you have Parallel Computing Toolbox, you can change the global execution environment by using the `mapreducer`

function.

Create a tall matrix `X`

containing a subset of variables from the `airlinesmall`

data set. See Percentiles of Tall Vector for Given Percentage for details about the steps to extract data from a tall array.

varnames = {'ArrDelay','ArrTime','DepTime','ActualElapsedTime'}; % Subset of variables in the data set ds = datastore('airlinesmall.csv','TreatAsMissing','NA',... 'SelectedVariableNames',varnames); % Datastore t = tall(ds); % Tall table

Starting parallel pool (parpool) using the 'local' profile ... Connected to the parallel pool (number of workers: 4).

`X = t{:,varnames} % Tall matrix`

X = Mx4 tall double matrix 8 735 642 53 8 1124 1021 63 21 2218 2055 83 13 1431 1332 59 4 746 629 77 59 1547 1446 61 3 1052 928 84 11 1134 859 155 : : : : : : : :

When operating along a dimension that is not 1, the `prctile`

function calculates the exact percentiles only, so that it can perform the computation efficiently using a sorting-based algorithm (see Algorithms) instead of an approximation algorithm based on T-Digest.

Calculate the exact 25th, 50th, and 75th percentiles of `X`

along the second dimension.

```
p = [25 50 75]; % Vector of percentages
Yexact = prctile(X,p,2)
```

Yexact = MxNx... tall double array ? ? ? ... ? ? ? ... ? ? ? ... : : : : : :

When the function operates along the first dimension and `p`

is a vector of percentages, you must use the approximation algorithm based on t-digest to compute the percentiles. Using the sorting-based algorithm to find the percentiles along the first dimension of a tall array is computationally intensive.

Calculate the approximate 25th, 50th, and 75th percentiles of `X`

along the first dimension. Because the default dimension is 1, you do not need to specify a value for `dim`

.

Yapprox = prctile(X,p,'Method','approximate')

Yapprox = MxNx... tall double array ? ? ? ... ? ? ? ... ? ? ? ... : : : : : :

Evaluate the tall arrays and bring the results into memory by using `gather`

.

[Yexact,Yapprox] = gather(Yexact,Yapprox);

Evaluating tall expression using the Parallel Pool 'local': - Pass 1 of 1: Completed in 12 sec Evaluation completed in 14 sec

Show the first five rows of the exact 25th, 50th, and 75th percentiles along the second dimension of `X`

.

Yexact(1:5,:)

ans =5×310^{3}× 0.0305 0.3475 0.6885 0.0355 0.5420 1.0725 0.0520 1.0690 2.1365 0.0360 0.6955 1.3815 0.0405 0.3530 0.6875

Each row of the matrix `Yexact`

contains the three percentiles of the corresponding row in `X`

. For example, `30.5`

, `347.5`

, and `688.5`

are the 25th, 50th, and 75th percentiles, respectively, of the first row in `X`

.

Show the approximate 25th, 50th, and 75th percentiles of `X`

along the first dimension.

Yapprox

Yapprox =3×410^{3}× -0.0070 1.1149 0.9322 0.0700 0 1.5220 1.3350 0.1020 0.0110 1.9180 1.7400 0.1510

Each column of the matrix `Yapprox`

corresponds to the three percentiles for each column of the matrix `X`

. For example, the first column of `Yapprox`

with elements (–7, 0, 11) contains the percentiles for the first column of `X`

.

`X`

— Input datavector | array

Input data, specified as a vector or array.

**Data Types: **`double`

| `single`

`p`

— Percentagesscalar | vector

Percentages for which to compute percentiles, specified as a scalar or vector of scalars from 0 to 100.

**Example: **25

**Example: **[25, 50, 75]

**Data Types: **`double`

| `single`

`dim`

— Dimensionpositive integer

Dimension along which the percentiles of `X`

are requested, specified as a
positive integer. For example, for a matrix `X`

, when
`dim`

= 1, `prctile`

returns the percentile(s) of
the columns of `X`

; when `dim`

= 2,
`prctile`

returns the percentile(s) of the rows of
`X`

. For a multidimensional array `X`

, the length of
the `dim`

th dimension of `Y`

is equal to the length of
`p`

.

**Data Types: **`double`

| `single`

`vecdim`

— Vector of dimensionspositive integer vector

Vector of dimensions, specified as a positive integer vector. Each element of
`vecdim`

represents a dimension of the input array
`X`

. The output `Y`

has length
`length(p)`

in the smallest specified operating dimension (that is,
dimension `min(vecdim)`

) and has length 1 in each of the remaining
operating dimensions. The other dimension lengths are the same for
`X`

and `Y`

.

For example, consider a 2-by-3-by-3 array `X`

with ```
p = [20 40
60 80]
```

. In this case, `prctile(X,p,[1 2])`

returns an
array, where each page of the array contains the 20th, 40th, 60th, and 80th percentiles
of the elements of the corresponding page of `X`

. Because 1 and 2 are
the operating dimensions, with `min([1 2]) = 1`

and
`length(p) = 4`

, the output is a 4-by-1-by-3 array.

**Data Types: **`single`

| `double`

`method`

— Method for calculating percentiles`'exact'`

(default) | `'approximate'`

Method for calculating percentiles, specified as `'exact'`

or
`'approximate'`

. By default, `prctile`

returns the
exact percentiles by implementing an algorithm that uses sorting. You can specify
`'method','approximate'`

for `prctile`

to return
approximate percentiles by implementing an algorithm that uses T-Digest.

**Data Types: **`char`

| `string`

`Y`

— Percentilesscalar | array

Percentiles of a data vector or array, returned as a scalar or array for one or more percentage values.

If

`X`

is a vector, then`Y`

is a scalar or a vector with the same length as the number of percentiles requested (`length(p)`

).`Y(i)`

contains the`p(i)`

th percentile.If

`X`

is an array of dimension*d*, then`Y`

is an array with the length of the smallest operating dimension equal to the number of percentiles requested (`length(p)`

).

A *multidimensional array* is
an array with more than two dimensions. For example, if X is a 1-by-3-by-4
array, then X is a 3-D array.

A *nonsingleton dimension* of an array is a dimension
whose size is not equal to 1. A *first nonsingleton dimension* of an
array is the first dimension that satisfies the nonsingleton condition. For example, if
`X`

is a 1-by-1-by-2-by-4 array, then the third dimension is the first
nonsingleton dimension of `X`

.

Linear interpolation uses linear polynomials
to find *y _{i}* = f(

$$y=f(x)={y}_{1}+\frac{\left(x-{x}_{1}\right)}{\left({x}_{2}-{x}_{1}\right)}\left({y}_{2}-{y}_{1}\right).$$

Similarly, if the 100(1.5/*n*)th percentile
is *y*_{1.5/n} and
the 100(2.5/*n*)th percentile is *y*_{2.5/n},
then linear interpolation finds the 100(2.3/*n*)th
percentile, *y*_{2.3/n} as:

$${y}_{\frac{2.3}{n}}={y}_{\frac{1.5}{n}}+\frac{\left(\frac{2.3}{n}-\frac{1.5}{n}\right)}{\left(\frac{2.5}{n}-\frac{1.5}{n}\right)}\left({y}_{\frac{2.5}{n}}-{y}_{\frac{1.5}{n}}\right).$$

T-digest[2] is a probabilistic data structure that is a sparse representation of the empirical cumulative distribution function (CDF) of a data set. T-digest is useful for computing approximations of rank-based statistics (such as percentiles and quantiles) from online or distributed data in a way that allows for controllable accuracy, particularly near the tails of the data distribution.

For data that is distributed in different partitions, t-digest computes quantile estimates
(and percentile estimates) for each data partition separately, and then combines the
estimates while maintaining a constant-memory bound and constant relative accuracy of
computation ($$q(1-q)$$ for the *q*th quantile). For these reasons, t-digest is
practical for working with tall arrays.

To estimate quantiles of an array that is distributed in different partitions, first build a
t-digest in each partition of the data. A t-digest clusters the data in the partition and
summarizes each cluster by a centroid value and an accumulated weight that represents the
number of samples contributing to the cluster. T-digest uses large clusters (widely spaced
centroids) to represent areas of the CDF that are near

and uses small clusters (tightly spaced centroids) to represent areas of
the CDF that are near *q* =
0.5

or
*q* = 0

.*q* = 1

T-digest controls the cluster size by using a scaling function that maps a quantile
*q* to an index *k* with a compression parameter $$\delta $$. That is,

$$k(q,\delta )=\delta \cdot \left(\frac{{\mathrm{sin}}^{-1}(2q-1)}{\pi}+\frac{1}{2}\right),$$

where the mapping *k* is monotonic with minimum value *k*(0,*δ*) = 0 and maximum value *k*(1,*δ*) =
*δ*. The following figure shows the scaling function for *δ* = 10.

The scaling function translates the quantile *q* to the scaling factor
*k* in order to give variable size steps in *q*. As a
result, cluster sizes are unequal (larger around the center quantiles and smaller near

or *q* = 0

). The smaller clusters allow for better accuracy near the edges of the
data.*q* =
1

To update a t-digest with a new observation that has a weight and location, find the cluster closest to the new observation. Then, add the weight and update the centroid of the cluster based on the weighted average, provided that the updated weight of the cluster does not exceed the size limitation.

You can combine independent t-digests from each partition of the data by taking a union of the t-digests and merging their centroids. To combine t-digests, first sort the clusters from all the independent t-digests in decreasing order of cluster weights. Then, merge neighboring clusters, when they meet the size limitation, to form a new t-digest.

Once you form a t-digest that represents the complete data set, you can estimate the end-points (or boundaries) of each cluster in the t-digest and then use interpolation between the end-points of each cluster to find accurate quantile estimates.

For an *n*-element vector `X`

, `prctile`

returns percentiles by using a sorting-based algorithm as follows:

The sorted elements in

`X`

are taken as the 100(0.5/*n*)th, 100(1.5/*n*)th, ..., 100([*n*– 0.5]/*n*)th percentiles. For example:For a data vector of five elements such as {6, 3, 2, 10, 1}, the sorted elements {1, 2, 3, 6, 10} respectively correspond to the 10th, 30th, 50th, 70th, and 90th percentiles.

For a data vector of six elements such as {6, 3, 2, 10, 8, 1}, the sorted elements {1, 2, 3, 6, 8, 10} respectively correspond to the (50/6)th, (150/6)th, (250/6)th, (350/6)th, (450/6)th, and (550/6)th percentiles.

`prctile`

uses linear interpolation to compute percentiles for percentages between 100(0.5/*n*) and 100([*n*– 0.5]/*n*).`prctile`

assigns the minimum or maximum values of the elements in`X`

to the percentiles corresponding to the percentages outside that range.

`prctile`

treats `NaN`

s
as missing values and removes them.

[1] Langford, E. “Quartiles in
Elementary Statistics”, *Journal of Statistics Education*. Vol.
14, No. 3, 2006.

[2] Dunning, T., and O. Ertl. “Computing Extremely Accurate Quantiles Using T-Digests.” August 2017.

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

Usage notes and limitations:

`Y = prctile(X,p)`

returns the exact percentiles (using a sorting-based algorithm) only if`X`

is a tall column vector.`Y = prctile(X,p,dim)`

returns the exact percentiles only when*one*of these conditions exists:`X`

is a tall column vector.`X`

is a tall array and`dim`

is not`1`

. For example,`prctile(X,p,2)`

returns the exact percentiles along the rows of the tall array`X`

.

If

`X`

is a tall array and`dim`

is`1`

, then you must specify`'Method','approximate'`

to use an approximation algorithm based on T-Digest for computing the percentiles. For example,`prctile(X,p,1,'Method','approximate')`

returns the approximate percentiles along the columns of the tall array`X`

.`Y = prctile(X,p,vecdim)`

returns the exact percentiles only when*one*of these conditions exists:`X`

is a tall column vector.`X`

is a tall array and`vecdim`

does not include`1`

. For example, if`X`

is a 3-by-5-by-2 array, then`prctile(X,p,[2,3])`

returns the exact percentiles of the elements in each`X(i,:,:)`

slice.`X`

is a tall array and`vecdim`

includes`1`

and all the nonsingleton dimensions of`X`

. For example, if`X`

is a 10-by-1-by-4 array, then`prctile(X,p,[1 3])`

returns the exact percentiles of the elements in`X(:,1,:)`

.

If

`X`

is a tall array and`vecdim`

includes`1`

but does not include all the nonsingleton dimensions of`X`

, then you must specify`'Method','approximate'`

to use the approximation algorithm. For example, if`X`

is a 10-by-1-by-4 array, you can use`prctile(X,p,[1 2],'Method','approximate')`

to find the approximate percentiles of each page of`X`

.

For more information, see Tall Arrays (MATLAB).

Generate C and C++ code using MATLAB® Coder™.

Usage notes and limitations:

The

`'all'`

and`vecdim`

input arguments are not supported.The

`'Method'`

name-value pair argument is not supported.The

`dim`

input argument must be a compile-time constant.If you do not specify the

`dim`

input argument, the working (or operating) dimension can be different in the generated code. As a result, run-time errors can occur. For more details, see Automatic dimension restriction (MATLAB Coder).If the output

`Y`

is a vector, the orientation of`Y`

differs from MATLAB^{®}when all of the following are true:You do not supply

`dim`

.`X`

is a variable-size array, and not a variable-size vector, at compile time, but`X`

is a vector at run time.The orientation of the vector

`X`

does not match the orientation of the vector`p`

.

In this case, the output

`Y`

matches the orientation of`X`

, not the orientation of`p`

.

For more information on code generation, see Introduction to Code Generation and General Code Generation Workflow.

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

Usage notes and limitations:

The

`'all'`

and`vecdim`

input arguments are not supported.The

`'Method'`

name-value pair argument is not supported.

For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

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