Documentation

histogram(X) creates a histogram plot of X. The histogram function uses an automatic binning algorithm that returns bins with a uniform width, chosen to cover the range of elements in X and reveal the underlying shape of the distribution. histogram displays the bins as rectangles such that the height of each rectangle indicates the number of elements in the bin.

histogram(X,nbins) uses a number of bins specified by the scalar, nbins.

histogram(X,edges) sorts X into bins with the bin edges specified by the vector, edges. Each bin includes the left edge, but does not include the right edge, except for the last bin which includes both edges.

histogram('BinEdges',edges,'BinCounts',counts) manually specifies bin edges and associated bin counts. histogram plots the specified bin counts and does not do any data binning.

histogram(C), where C is a categorical array, plots a histogram with a bar for each category in C.

histogram(C,Categories) plots only the subset of categories specified by Categories.

histogram('Categories',Categories,'BinCounts',counts) manually specifies categories and associated bin counts. histogram plots the specified bin counts and does not do any data binning.

histogram(___,Name,Value) specifies additional options with one or more Name,Value pair arguments using any of the previous syntaxes. For example, you can specify 'BinWidth' and a scalar to adjust the width of the bins, or 'Normalization' with a valid option ('count', 'probability', 'countdensity', 'pdf', 'cumcount', or 'cdf') to use a different type of normalization.

histogram(ax,___) plots into the axes specified by ax instead of into the current axes (gca). The option ax can precede any of the input argument combinations in the previous syntaxes.

h = histogram(___) returns a Histogram object. Use this to inspect and adjust the properties of the histogram.

Examples

Histogram of Vector

Generate 10,000 random numbers and create a histogram. The histogram function automatically chooses an appropriate number of bins to cover the range of values in x and show the shape of the underlying distribution.

x = randn(10000,1);
h = histogram(x)

h = 

  Histogram with properties:

             Data: [10000×1 double]
           Values: [1×37 double]
          NumBins: 37
         BinEdges: [1×38 double]
         BinWidth: 0.2000
        BinLimits: [-3.8000 3.6000]
    Normalization: 'count'
        FaceColor: 'auto'
        EdgeColor: [0 0 0]

  Use GET to show all properties

When you specify an output argument to the histogram function, it returns a histogram object. You can use this object to inspect the properties of the histogram, such as the number of bins or the width of the bins.

Find the number of histogram bins.

nbins = h.NumBins

nbins =

    37

Specify Number of Histogram Bins

Plot a histogram of 1,000 random numbers sorted into 25 equally spaced bins.

x = randn(1000,1);
nbins = 25;
h = histogram(x,nbins)

h = 

  Histogram with properties:

             Data: [1000×1 double]
           Values: [1×25 double]
          NumBins: 25
         BinEdges: [1×26 double]
         BinWidth: 0.2800
        BinLimits: [-3.4000 3.6000]
    Normalization: 'count'
        FaceColor: 'auto'
        EdgeColor: [0 0 0]

  Use GET to show all properties

Find the bin counts.

counts = h.Values

counts =

  Columns 1 through 13

     1     3     0     6    14    19    31    54    74    80    92   122   104

  Columns 14 through 25

   115    88    80    38    32    21     9     5     5     5     0     2

Change Number of Histogram Bins

Generate 1,000 random numbers and create a histogram.

X = randn(1000,1);
h = histogram(X)

h = 

  Histogram with properties:

             Data: [1000×1 double]
           Values: [1×23 double]
          NumBins: 23
         BinEdges: [1×24 double]
         BinWidth: 0.3000
        BinLimits: [-3.3000 3.6000]
    Normalization: 'count'
        FaceColor: 'auto'
        EdgeColor: [0 0 0]

  Use GET to show all properties

Use the morebins function to coarsely adjust the number of bins.

Nbins = morebins(h);
Nbins = morebins(h)

Nbins =

    29

Adjust the bins at a fine grain level by explicitly setting the number of bins.

h.NumBins = 31;

Specify Bin Edges of Histogram

Generate 1,000 random numbers and create a histogram. Specify the bin edges as a vector with wide bins on the edges of the histogram to capture the outliers that do not satisfy . The first vector element is the left edge of the first bin, and the last vector element is the right edge of the last bin.

x = randn(1000,1);
edges = [-10 -2:0.25:2 10];
h = histogram(x,edges);

Specify the Normalization property as 'countdensity' to flatten out the bins containing the outliers. Now, the area of each bin (rather than the height) represents the frequency of observations in that interval.

h.Normalization = 'countdensity';

Plot Categorical Histogram

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 = 

  Columns 1 through 9

     no      no      yes      yes      yes      no      no      no      no 

  Columns 10 through 16

     undecided      undecided      yes      no      no      no      yes 

  Columns 17 through 25

     no      yes      no      yes      no      no      no      yes      yes 

  Columns 26 through 27

     yes      yes

Plot a categorical histogram of the votes, using a relative bar width of 0.5.

h = histogram(C,'BarWidth',0.5)

h = 

  Histogram with properties:

             Data: [1×27 categorical]
           Values: [11 14 2]
       Categories: {'yes'  'no'  'undecided'}
    Normalization: 'count'
     DisplayStyle: 'bar'
        FaceColor: 'auto'
        EdgeColor: [0 0 0]

  Use GET to show all properties

Histogram with Specified Normalization

Generate 1,000 random numbers and create a histogram using the 'probability' normalization.

x = randn(1000,1);
h = histogram(x,'Normalization','probability')

h = 

  Histogram with properties:

             Data: [1000×1 double]
           Values: [1×23 double]
          NumBins: 23
         BinEdges: [1×24 double]
         BinWidth: 0.3000
        BinLimits: [-3.3000 3.6000]
    Normalization: 'probability'
        FaceColor: 'auto'
        EdgeColor: [0 0 0]

  Use GET to show all properties

Compute the sum of the bar heights. With this normalization, the height of each bar is equal to the probability of selecting an observation within that bin interval, and the height of all of the bars sums to 1.

S = sum(h.Values)

S =

     1

Plot Multiple Histograms

Generate two vectors of random numbers and plot a histogram for each vector in the same figure.

x = randn(2000,1);
y = 1 + randn(5000,1);
h1 = histogram(x);
hold on
h2 = histogram(y);

Since the sample size and bin width of the histograms are different, it is difficult to compare them. Normalize the histograms so that all of the bar heights add to 1, and use a uniform bin width.

h1.Normalization = 'probability';
h1.BinWidth = 0.25;
h2.Normalization = 'probability';
h2.BinWidth = 0.25;

Adjust Histogram Properties

Generate 1,000 random numbers and create a histogram. Return the histogram object to adjust the properties of the histogram without recreating the entire plot.

x = randn(1000,1);
h = histogram(x)

h = 

  Histogram with properties:

             Data: [1000×1 double]
           Values: [1×23 double]
          NumBins: 23
         BinEdges: [1×24 double]
         BinWidth: 0.3000
        BinLimits: [-3.3000 3.6000]
    Normalization: 'count'
        FaceColor: 'auto'
        EdgeColor: [0 0 0]

  Use GET to show all properties

Specify exactly how many bins to use.

h.NumBins = 15;

Specify the edges of the bins with a vector. The first value in the vector is the left edge of the first bin. The last value is the right edge of the last bin.

h.BinEdges = [-3:3];

Change the color of the histogram bars.

h.FaceColor = [0 0.5 0.5];
h.EdgeColor = 'r';

Determine Underlying Probability Distribution

Generate 5,000 normally distributed random numbers with a mean of 5 and a standard deviation of 2. Plot a histogram with Normalization set to 'pdf' to produce an estimation of the probability density function.

x = 2*randn(5000,1) + 5;
histogram(x,'Normalization','pdf')

In this example, the underlying distribution for the normally distributed data is known. You can, however, use the 'pdf' histogram plot to determine the underlying probability distribution of the data by comparing it against a known probability density function.

The probability density function for a normal distribution with mean $\mu$ , standard deviation $\sigma$ , and variance $\sigma^2$ is

$f(x,\mu,\sigma) = \frac{1}{\sigma \sqrt{2 \pi}} \mbox{ exp}
\left[-\frac{(x-\mu)^2}{2\sigma^2}\right].$

Overlay a plot of the probability density function for a normal distribution with a mean of 5 and a standard deviation of 2.

hold on
y = -5:0.1:15;
mu = 5;
sigma = 2;
f = exp(-(y-mu).^2./(2*sigma^2))./(sigma*sqrt(2*pi));
plot(y,f,'LineWidth',1.5)

Related Examples

Plot Categorical Data

Input Arguments

`X` — Data 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 histogram treats it as a single column vector, X(:), and plots a single histogram.

histogram ignores all NaN values. Similarly, histogram ignores Inf and -Inf values, unless the bin edges explicitly specify Inf or -Inf as a bin edge.

Note: If X contains integers of type int64 or uint64 that are larger than flintmax, then it is recommended that you explicitly specify the histogram bin edges. histogram automatically bins the input data using double precision, which lacks integer precision for numbers greater than flintmax.

`C` — Categorical data
categorical array

Categorical data, specified as a categorical array. histogram ignores undefined categorical values.

Data Types: categorical

`nbins` — Number of bins
positive integer

Number of bins, specified as a positive integer. If you do not specify nbins, then histogram automatically calculates how many bins to use based on the values in X.

Example: histogram(X,15) creates a histogram with 15 bins.

`edges` — Bin edges
vector

Bin edges, specified as a vector. edges(1) is the left edge of the first bin, and edges(end) is the right edge of the last bin.

The value X(i) is in the kth bin if edges(k) ≤ X(i) < edges(k+1). The last bin also includes the right bin edge, so that it contains X(i) if edges(end-1) ≤ X(i) ≤ edges(end).

`Categories` — Categories included in histogram
cell array of character vectors | categorical vector

Note: This option only applies to categorical histograms.

Categories included in histogram, specified as a cell array of character vectors or categorical vector.

If you specify an input categorical array C, then by default, histogram plots a bar for each category in C. In that case, use Categories to specify a unique subset of the categories instead.
If you specify bin counts, then Categories specifies the associated category names for the histogram.

Example: h = histogram(C,{'Large','Small'}) plots only the categorical data in the categories 'Large' and 'Small'.

Example: histogram('Categories',{'Yes','No','Maybe'},'BinCounts',[22 18 3]) plots a histogram that has three categories with the associated bin counts.

Example: h.Categories queries the categories that are in histogram object h.

Data Types: cell | categorical

`counts` — Bin counts
vector

Bin counts, specified as a vector. Use this input to pass bin counts to histogram when the bin counts calculation is performed separately and you do not want histogram to do any data binning.

The length of counts must be equal to the number of bins.

For numeric histograms, the number of bins is length(edges)-1.
For categorical histograms, the number of bins is equal to the number of categories.

Example: histogram('BinEdges',-2:2,'BinCounts',[5 8 15 9])

Example: histogram('Categories',{'Yes','No','Maybe'},'BinCounts',[22 18 3])

`ax` — Axes object
axes object

Axes object. If you do not specify an axes, then the histogram function uses the current axes (gca).

Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside single quotes (' '). You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

Example: histogram(X,'BinWidth',5)

The histogram properties listed here are only a subset. For a complete list, see Histogram Properties.

`'BarWidth'` — Relative width of categorical bars
`0.9` (default) | scalar in range `[0,1]`

Note: This option only applies to histograms of categorical data.

Relative width of categorical bars, specified as a scalar value in the range [0,1]. Use this property to control the separation of categorical bars within the histogram. The default value is 0.9, which means that the bar width is 90% of the space from the previous bar to the next bar, with 5% of that space on each side.

If you set this property to 1, then adjacent bars touch.

Example: 0.5

`'BinLimits'` — Bin limits
two-element vector

Bin limits, specified as a two-element vector, [bmin,bmax]. This option plots a histogram using the values in the input array, X, that fall between bmin and bmax inclusive. That is, X(X>=bmin & X<=bmax).

This option does not apply to histograms of categorical data.

Example: histogram(X,'BinLimits',[1,10]) plots a histogram using only the values in X that are between 1 and 10 inclusive.

`'BinLimitsMode'` — Selection mode for bin limits
`'auto'` (default) | `'manual'`

Selection mode for bin limits, specified as 'auto' or 'manual'. The default value is 'auto', so that the bin limits automatically adjust to the data.

If you explicitly specify either BinLimits or BinEdges, then BinLimitsMode is automatically set to 'manual'. In that case, specify BinLimitsMode as 'auto' to rescale the bin limits to the data.

This option does not apply to histograms of categorical data.

`'BinMethod'` — Binning algorithm
`'auto'` (default) | `'scott'` | `'fd'` | `'integers'` | `'sturges'` | `'sqrt'`

Binning algorithm, specified as one of the values in this table.

Value	Description
`'auto'`	The default `'auto'` algorithm chooses a bin width to cover the data range and reveal the shape of the underlying distribution.
`'scott'`	Scott's rule is optimal if the data is close to being normally distributed. This rule is appropriate for most other distributions, as well. It uses a bin width of `3.5std(X(:))numel(X)^(-1/3)`.
`'fd'`	The Freedman-Diaconis rule is less sensitive to outliers in the data, and might be more suitable for data with heavy-tailed distributions. It uses a bin width of `2IQR(X(:))numel(X)^(-1/3)`, where `IQR` is the interquartile range of `X`.
`'integers'`	The integer rule is useful with integer data, as it creates a bin for each integer. It uses a bin width of 1 and places bin edges halfway between integers. To avoid accidentally creating too many bins, you can use this rule to create a limit of 65536 bins (2¹⁶). If the data range is greater than 65536, then the integer rule uses wider bins instead.
`'sturges'`	Sturges' rule is popular due to its simplicity. It chooses the number of bins to be `ceil(1 + log2(numel(X)))`.
`'sqrt'`	The Square Root rule is widely used in other software packages. It chooses the number of bins to be `ceil(sqrt(numel(X)))`.

This option does not apply to histograms of categorical data.

Note: If you set the BinLimits, NumBins, BinEdges, or BinWidth property, then the BinMethod property is set to 'manual'.

Example: histogram(X,'BinMethod','integers') creates a histogram with the bins centered on integers.

`'BinWidth'` — Width of bins
scalar

Width of bins, specified as a scalar. When you specify BinWidth, then histogram can use a maximum of 65,536 bins (or 2¹⁶). If instead the specified bin width requires more bins, then histogram uses a larger bin width corresponding to the maximum number of bins.

This option does not apply to histograms of categorical data.

Example: histogram(X,'BinWidth',5) uses bins with a width of 5.

`'DisplayStyle'` — Histogram display style
`'bar'` (default) | `'stairs'`

Histogram display style, specified as either 'bar' or 'stairs'. Specify 'stairs' to display a stairstep plot, which displays the outline of the histogram without filling the interior.

The default value of 'bar' displays a histogram bar plot.

Example: histogram(X,'DisplayStyle','stairs') plots the outline of the histogram.

`'EdgeAlpha'` — Transparency of histogram bar edges
`1` (default) | scalar value between `0` and `1` inclusive

Transparency of histogram bar edges, specified as a scalar value between 0 and 1 inclusive. A value of 1 means fully opaque and 0 means completely transparent (invisible).

Example: histogram(X,'EdgeAlpha',0.5) creates a histogram plot with semi-transparent bar edges.

`'EdgeColor'` — Histogram edge color
`[0 0 0]` or black (default) | `'none'` | `'auto'` | RGB triplet or color name

Histogram edge color, specified as one of these values:

'none' — Edges are not drawn.
'auto' — Color of each edge is chosen automatically.
RGB triplet or a color name — Edges use the specified color.
An RGB triplet is a three-element row vector whose elements specify the intensities of the red, green, and blue components of the color. The intensities must be in the range [0,1]; for example, [0.4 0.6 0.7]. This table lists the long and short color name options and the equivalent RGB triplet values.
Long Name Short Name RGB Triplet

'yellow' 'y' [1 1 0]

'magenta' 'm' [1 0 1]

'cyan' 'c' [0 1 1]

'red' 'r' [1 0 0]

'green' 'g' [0 1 0]

'blue' 'b' [0 0 1]

'white' 'w' [1 1 1]

'black' 'k' [0 0 0]

Long Name	Short Name	RGB Triplet
`'yellow'`	`'y'`	`[1 1 0]`
`'magenta'`	`'m'`	`[1 0 1]`
`'cyan'`	`'c'`	`[0 1 1]`
`'red'`	`'r'`	`[1 0 0]`
`'green'`	`'g'`	`[0 1 0]`
`'blue'`	`'b'`	`[0 0 1]`
`'white'`	`'w'`	`[1 1 1]`
`'black'`	`'k'`	`[0 0 0]`

Example: histogram(X,'EdgeColor','r') creates a histogram plot with red bar edges.

`'FaceAlpha'` — Transparency of histogram bars
`0.6` (default) | scalar value between `0` and `1` inclusive

Transparency of histogram bars, specified as a scalar value between 0 and 1 inclusive. histogram uses the same transparency for all the bars of the histogram. A value of 1 means fully opaque and 0 means completely transparent (invisible).

Example: histogram(X,'FaceAlpha',1) creates a histogram plot with fully opaque bars.

`'FaceColor'` — Histogram bar color
`'auto'` (default) | `'none'` | RGB triplet or color name

Histogram bar color, specified as one of these values:

'none' — Bars are not filled.
'auto' — Histogram bar color is chosen automatically (default).
RGB triplet or a color name — Bars are filled with the specified color.
An RGB triplet is a three-element row vector whose elements specify the intensities of the red, green, and blue components of the color. The intensities must be in the range [0,1]; for example, [0.4 0.6 0.7]. This table lists the long and short color name options and the equivalent RGB triplet values.
Long Name Short Name RGB Triplet

'yellow' 'y' [1 1 0]

'magenta' 'm' [1 0 1]

'cyan' 'c' [0 1 1]

'red' 'r' [1 0 0]

'green' 'g' [0 1 0]

'blue' 'b' [0 0 1]

'white' 'w' [1 1 1]

'black' 'k' [0 0 0]

Long Name	Short Name	RGB Triplet
`'yellow'`	`'y'`	`[1 1 0]`
`'magenta'`	`'m'`	`[1 0 1]`
`'cyan'`	`'c'`	`[0 1 1]`
`'red'`	`'r'`	`[1 0 0]`
`'green'`	`'g'`	`[0 1 0]`
`'blue'`	`'b'`	`[0 0 1]`
`'white'`	`'w'`	`[1 1 1]`
`'black'`	`'k'`	`[0 0 0]`

If you specify DisplayStyle as 'stairs', then histogram does not use the FaceColor property.

Example: histogram(X,'FaceColor','g') creates a histogram plot with green bars.

`'LineStyle'` — Line style
`'-'` (default) | `'--'` | `':'` | `'-.'` | `'none'`

Line style, specified as one of the line styles listed in this table.

Line Style	Description	Resulting Line
`'-'`	Solid line
`'--'`	Dashed line
`':'`	Dotted line
`'-.'`	Dash-dotted line
`'none'`	No line	No line

`'LineWidth'` — Width of bar outlines
`0.5` (default) | positive value

Width of bar outlines, specified as a positive value in point units. One point equals 1/72 inch.

Example: 1.5

`'Normalization'` — Type of normalization
`'count'` (default) | `'probability'` | `'countdensity'` | `'pdf'` | `'cumcount'` | `'cdf'`

Type of normalization, specified as one of the values in this table.

Value	Description
`'count'`	Default normalization scheme. The height of each bar is the number of observations in each bin. The sum of the bar heights is `numel(X)`. For categorical histograms, the sum of the bar heights is either `numel(X)` or `sum(ismember(X(:),Categories))`.
`'probability'`	The height of each bar is the relative number of observations, (number of observations in bin / total number of observations). The sum of the bar heights is `1`. For categorical histograms, the height of each bar is, (number of elements in category / total number of elements in all categories). The sum of the bar heights is `1`.
`'countdensity'`	The height of each bar is, (number of observations in bin / width of bin). The area (height * width) of each bar is the number of observations in the bin. The sum of the bar areas is `numel(X)`. For categorical histograms, this is the same as `'count'`.
`'pdf'`	Probability density function estimate. The height of each bar is, (number of observations in the bin) / (total number of observations * width of bin). The area of each bar is the relative number of observations. The sum of the bar areas is `1`. For categorical histograms, this is the same as `'probability'`.
`'cumcount'`	The height of each bar is the cumulative number of observations in each bin and all previous bins. The height of the last bar is `numel(X)`. For categorical histograms, the height of each bar is equal to the cumulative number of elements in each category and all previous categories. The height of the last bar is `numel(X)` or `sum(ismember(X(:),Categories))`.
`'cdf'`	Cumulative density function estimate. The height of each bar is equal to the cumulative relative number of observations in the bin and all previous bins. The height of the last bar is `1`. For categorical data, the height of each bar is equal to the cumulative relative number of observations in each category and all previous categories. The height of the last bar is `1`.

Example: histogram(X,'Normalization','pdf') plots an estimate of the probability density function for X.

`'Orientation'` — Orientation of bars
`'vertical'` (default) | `'horizontal'`

Orientation of bars, specified as 'vertical' or 'horizontal'.

Example: histogram(X,'Orientation','horizontal') creates a histogram plot with horizontal bars.

Output Arguments

`h` — Histogram
object

Histogram, returned as an object. For more information, see histogram.

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