MATLAB Function Reference    

Histogram plot



A histogram shows the distribution of data values.

n = hist(Y) bins the elements in vector Y into 10 equally spaced containers and returns the number of elements in each container as a row vector. If Y is an m-by-p matrix, hist treats the columns of Y as vectors and returns a 10-by-p matrix n. Each column of n contains the results for the corresponding column of Y.

n = hist(Y,x) where x is a vector, returns the distribution of Y among length(x) bins with centers specified by x. For example, if x is a 5-element vector, hist distributes the elements of Y into five bins centered on the x-axis at the elements in x. Note: use histc if it is more natural to specify bin edges instead of centers.

n = hist(Y,nbins) where nbins is a scalar, uses nbins number of bins.

[n,xout] = hist(...) returns vectors n and xout containing the frequency counts and the bin locations. You can use bar(xout,n) to plot the histogram.

hist(...) without output arguments, hist produces a histogram plot of the output described above. hist distributes the bins along the x-axis between the minimum and maximum values of Y.


All elements in vector Y or in one column of matrix Y are grouped according to their numeric range. Each group is shown as one bin.

The histogram's x-axis reflects the range of values in Y. The histogram's y-axis shows the number of elements that fall within the groups; therefore, the y-axis ranges from 0 to the greatest number of elements deposited in any bin.

The histogram is created with a patch graphics object. If you want to change the color of the graph, you can set patch properties. See the "Example" section for more information. By default, the graph color is controlled by the current colormap, which maps the bin color to the first color in the colormap.


Generate a bell-curve histogram from Gaussian data.

Change the color of the graph so that the bins are red and the edges of the bins are white.

See Also

bar, ColorSpec, histc, patch, rose, stairs

Specialized Plotting for related functions

Histograms for examples

  hilb histc