Histogram bin counts - MATLAB histcounts (2024)

Syntax Description Examples Bin Counts and Bin Edges Specify Number of Bins Specify Bin Edges Normalized Bin Counts Determine Bin Placement Categorical Bin Counts Input Arguments X — Data to distribute among bins vector | matrix | multidimensional array C — Categorical data categorical array nbins — Number of bins positive integer edges — Bin edges vector Categories — Categories included in count all categories (default) | string vector | cell vector of character vectors | pattern scalar | categorical vector Name-Value Arguments Output Arguments N — Bin counts row vector edges — Bin edges vector bin — Bin indices array Categories — Categories included in count cell vector of character vectors Tips 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™. GPU Code Generation Generate CUDA® code for NVIDIA® GPUs using GPU 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™. Version History R2023b: Normalize using percentages R2023a: Improved performance with small numeric and logical input data See Also Topics MATLAB Command Americas Europe Asia Pacific

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

X = [2 3 5 7 11 13 17 19 23 29];[N,edges] = histcounts(X,6)

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

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

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

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

count = nnz(bin==3)

count = 8

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 

[N,Categories] = histcounts(C)

N = 1×3 11 14 2

Categories = 1x3 cell {'yes'} {'no'} {'undecided'}

function timingHistcountsX = rand(1,1000);for k = 1:3e3 histcounts(X,"BinMethod","auto");endend

timeit(@timingHistcounts)