Histogram bin counts - MATLAB histcounts - MathWorks France (2024)

Histogram bin counts

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[N,edges]= histcounts(X)

[N,edges]= histcounts(X,nbins)

[N,edges]= histcounts(X,edges)

[N,edges,bin]= histcounts(___)

N = histcounts(C)

N = histcounts(C,Categories)

[N,Categories]= histcounts(___)

[___] = histcounts(___,Name,Value)



[N,edges]= histcounts(X) partitions the X values into bins and returns the bin counts and the bin edges. The histcounts function uses an automatic binning algorithm that returns uniform bins chosen to cover the range of elements in X and reveal the underlying shape of the distribution.


[N,edges]= histcounts(X,nbins) usesa number of bins specified by the scalar, nbins.


[N,edges]= histcounts(X,edges) sorts X into bins with the bin edges specified by the vector, edges.


[N,edges,bin]= histcounts(___) also returns an index array, bin,using any of the previous syntaxes. bin is an arrayof the same size as X whose elements are the binindices for the corresponding elements in X. Thenumber of elements in the kth bin is nnz(bin==k),which is the same as N(k).


N = histcounts(C),where C is a categorical array, returns a vector, N,that indicates the number of elements in C whosevalue is equal to each of C’s categories. N hasone element for each category in C.

N = histcounts(C,Categories) countsonly the elements in C whose value is equal tothe subset of categories specified by Categories.


[N,Categories]= histcounts(___) also returns the categoriesthat correspond to each count in N using eitherof the previous syntaxes for categorical arrays.


[___] = histcounts(___,Name,Value) specifies additional parameters using one or more name-value arguments. For example, you can specify BinWidth as a scalar to adjust the width of the bins for numeric data.


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Bin Counts and Bin Edges

Open Live Script

Distribute 100 random values into bins. histcounts automatically chooses an appropriate bin width to reveal the underlying distribution of the data.

X = randn(100,1);[N,edges] = histcounts(X)
N = 1×7 2 17 28 32 16 3 2
edges = 1×8 -3 -2 -1 0 1 2 3 4

Specify Number of Bins

Open Live Script

Distribute 10 numbers into 6 equally spaced bins.

N = 1×6 2 2 2 2 1 1
edges = 1×7 0 4.9000 9.8000 14.7000 19.6000 24.5000 29.4000

Specify Bin Edges

Open Live Script

Distribute 1,000 random numbers into bins. Define the bin edges with a vector, where the first element is the left edge of the first bin, and the last element is the right edge of the last bin.

X = randn(1000,1);edges = [-5 -4 -2 -1 -0.5 0 0.5 1 2 4 5];N = histcounts(X,edges)
N = 1×10 0 24 149 142 195 200 154 111 25 0

Normalized Bin Counts

Open Live Script

Distribute all of the prime numbers less than 100 into bins. Specify 'Normalization' as 'probability' to normalize the bin counts so that sum(N) is 1. That is, each bin count represents the probability that an observation falls within that bin.

X = primes(100);[N,edges] = histcounts(X, 'Normalization', 'probability')
N = 1×4 0.4000 0.2800 0.2800 0.0400
edges = 1×5 0 30 60 90 120

Determine Bin Placement

Open Live Script

Distribute 100 random integers between -5 and 5 into bins, and specify 'BinMethod' as 'integers' to use unit-width bins centered on integers. Specify a third output for histcounts to return a vector representing the bin indices of the data.

X = randi([-5,5],100,1);[N,edges,bin] = histcounts(X,'BinMethod','integers');

Find the bin count for the third bin by counting the occurrences of the number 3 in the bin index vector, bin. The result is the same as N(3).

count = nnz(bin==3)
count = 8

Categorical Bin Counts

Open Live Script

Create a categorical vector that represents votes. The categories in the vector are 'yes', 'no', or 'undecided'.

A = [0 0 1 1 1 0 0 0 0 NaN NaN 1 0 0 0 1 0 1 0 1 0 0 0 1 1 1 1];C = categorical(A,[1 0 NaN],{'yes','no','undecided'})
C = 1x27 categorical no no yes yes yes no no no no undecided undecided yes no no no yes no yes no yes no no no yes yes yes yes 

Determine the number of elements that fall into each category.

[N,Categories] = histcounts(C)
N = 1×3 11 14 2
Categories = 1x3 cell {'yes'} {'no'} {'undecided'}

Input Arguments

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XData to distribute among bins
vector | matrix | multidimensional array

Data to distribute among bins, specified as a vector, matrix,or multidimensional array. If X is not a vector,then histcounts treats it as a single column vector, X(:).

histcounts ignores all NaN values.Similarly, histcounts ignores Inf and -Inf valuesunless the bin edges explicitly specify Inf or -Inf asa bin edge.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | logical | datetime | duration

CCategorical data
categorical array

Categorical data, specified as a categorical array. histcounts ignoresundefined categorical values.

Data Types: categorical

nbinsNumber of bins
positive integer

Number of bins, specified as a positive integer. If you do notspecify nbins, then histcounts automaticallycalculates how many bins to use based on the values in X.

Example: [N,edges] = histcounts(X,15) uses15 bins.

edgesBin edges

Bin edges, specified as a vector. The first vector element specifies the leading edge of the first bin. The last element specifies the trailing edge of the last bin. The trailing edge is only included for the last bin.

For datetime and duration data, edges mustbe a datetime or duration vector in monotonically increasing order.

CategoriesCategories included in count
all categories (default) | string vector | cell vector of character vectors | pattern scalar | categorical vector

Categories included in count, specified as a string vector, cell vector of character vectors, pattern scalar, or categorical vector. By default, histcounts uses a bin for each category in categorical array C. Use Categories to specify a unique subset of the categories instead.

Example: h = histcounts(C,["Large","Small"]) counts only the categorical data in the categories Large and Small.

Example: h = histcounts(C,"Y" + wildcardPattern) counts categorical data in all the categories whose names begin with the letter Y.

Data Types: string | cell | pattern | categorical

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Before R2021a, use commas to separate each name and value, and enclose Name in quotes.

Example: [N,edges] = histcounts(X,'Normalization','probability') normalizesthe bin counts in N, such that sum(N) is1.

Output Arguments

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N — Bin counts
row vector

Bin counts, returned as a row vector.

edges — Bin edges

Bin edges, returned as a vector. The first element is the leading edge of the first bin. The last element is the trailing edge of the last bin.

bin — Bin indices

Bin indices, returned as an array of the same size as X.Each element in bin describes which numbered bincontains the corresponding element in X.

A value of 0 in bin indicatesan element which does not belong to any of the bins (for example,a NaN value).

Categories — Categories included in count
cell vector of character vectors

Categories included in count, returned as a cell vector of charactervectors. Categories contains the categories in C thatcorrespond to each count in N.


  • The behavior of histcounts issimilar to that of the discretize function. Use histcounts tofind the number of elements in each bin. On the other hand, use discretize tofind which bin each element belongs to (without counting).

Extended Capabilities

Version History

Introduced in R2014b

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You can normalize histogram values as percentages by specifying the Normalization name-value argument as 'percentage'.

See Also

histogram | histogram2 | discretize | histcounts2 | kde


  • Replace Discouraged Instances of hist and histc

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Histogram bin counts - MATLAB histcounts
- MathWorks France (2024)
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