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histogram

Histogram plot

  • Histogram plot

Description

Histograms are a type of bar plot that group data into bins. After you create a Histogram object, you can modify aspects of the histogram by changing its property values. This is particularly useful for quickly modifying the properties of the bins or changing the display.

Creation

Description

example

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 rectangular bars such that the height of each rectangle indicates the number of elements in the bin.

example

histogram(X,nbins) specifies the number of bins.

example

histogram(X,edges) sorts X into bins with bin edges specified in a vector.

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

example

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

histogram(C,Categories) plots only a subset of categories in C.

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.

example

histogram(___,Name,Value) specifies additional parameters using one or more name-value arguments for any of the previous syntaxes. For example, specify Normalization to use a different type of normalization. For a list of properties, see Histogram Properties.

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

example

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

Input Arguments

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Data to distribute among bins, specified as a vector, matrix, or multidimensional array. histogram treats matrix and multidimensional array data as a single column vector, X(:), and plots a single histogram.

histogram ignores all NaN and NaT values. Similarly, histogram ignores Inf and -Inf values, unless the bin edges explicitly specify Inf or -Inf as a bin edge. Although NaN, NaT, Inf, and -Inf values are typically not plotted, they are still included in normalization calculations that include the total number of data elements, such as 'probability'.

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.

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

Categorical data, specified as a categorical array. histogram does not plot undefined categorical values. However, undefined categorical values are still included in normalization calculations that include the total number of data elements, such as 'probability'.

Data Types: categorical

Number of bins, specified as a positive integer. If you do not specify nbins, then histogram determines the number of bins from the values in X.

If you specify nbins with BinMethod, BinWidth or BinEdges, histogram only honors the last parameter.

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

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

Each bin includes the leading edge, but does not include the trailing edge, except for the last bin which includes both edges.

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

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

Note

This option only applies to categorical histograms.

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

  • 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(C,"Y" + wildcardPattern) plots data in the categories whose names begin with the letter Y.

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 | string | pattern

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 size 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])

Target axes, specified as an Axes object or a PolarAxes object. If you do not specify the axes and if the current axes are Cartesian axes, then the histogram function uses the current axes (gca). To plot into polar axes, specify the PolarAxes object as the first input argument or use the polarhistogram function.

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.

Example: histogram(X,BinWidth=5)

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

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

Note

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

Bins

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

  • For datetime and duration data, BinWidth can be a scalar duration or calendar duration.

  • If you specify BinWidth with BinMethod, NumBins, or BinEdges, histogram only honors the last parameter.

  • This option does not apply to categorical data.

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

Bin limits, specified as a two-element vector, [bmin,bmax]. The first element indicates the first bin edge. The second element indicates the last bin edge.

This option computes using only the data that falls within the bin limits inclusively, X>=bmin & X<=bmax.

This option does not apply to categorical data.

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

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 specify BinLimits or BinEdges, then BinLimitsMode is set to 'manual'. Specify BinLimitsMode as 'auto' to rescale the bin limits to the data.

  • This option does not apply to histograms of categorical data.

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 3.5*std(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 2*iqr(X(:))*numel(X)^(-1/3).

'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.

'integers' does not support datetime or duration data.

'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))).

histogram adjusts the number of bins slightly so that the bin edges fall on "nice" numbers, rather than using these exact formulas.

For datetime or duration data, specify the bin width as one of these units of time.

ValueDescriptionData Type
"second"

Each bin is 1 second.

datetime and duration
"minute"

Each bin is 1 minute.

datetime and duration
"hour"

Each bin is 1 hour.

datetime and duration
"day"

Each bin is 1 calendar day. This value accounts for daylight saving time shifts.

datetime and duration
"week"Each bin is 1 calendar week.datetime only
"month"Each bin is 1 calendar month.datetime only
"quarter"Each bin is 1 calendar quarter.datetime only
"year"

Each bin is 1 calendar year. This value accounts for leap days.

datetime and duration
"decade"Each bin is 1 decade (10 calendar years).datetime only
"century"Each bin is 1 century (100 calendar years).datetime only

  • If you specify BinMethod for datetime or duration data, then histogram can use a maximum of 65,536 bins (or 216). If the specified bin duration requires more bins, then histogram uses a larger bin width corresponding to the maximum number of bins.

  • If you specify BinLimits, NumBins, BinEdges, or BinWidth, then BinMethod is set to 'manual'.

  • If you specify BinMethod with BinWidth, NumBins or BinEdges, histogram only honors the last parameter.

  • This option does not apply to categorical data.

Example: histogram(X,'BinMethod','integers') centers the bins on integers.

Categories

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Category display order, specified as 'data', 'ascend', or 'descend'.

  • 'data' — Use the category order in the input data C.

  • 'ascend' — Display the histogram with increasing bar heights.

  • 'descend' — Display the histogram with decreasing bar heights.

This option only works with categorical data.

Number of categories to display, specified as a scalar. You can change the ordering of categories displayed in the histogram using the 'DisplayOrder' option.

This option only works with categorical data.

Toggle summary display of data belonging to undisplayed categories, specified as 'on' or 'off', or as numeric or logical 1 (true) or 0 (false). A value of 'on' is equivalent to true, and 'off' is equivalent to false. Thus, you can use the value of this property as a logical value. The value is stored as an on/off logical value of type matlab.lang.OnOffSwitchState.

  • Set this option to 'on' to display an additional bar in the histogram with the name 'Others'. This extra bar counts all elements that do not belong to categories displayed in the histogram.

  • You can change the number of categories displayed in the histogram, as well as their order, using the 'NumDisplayBins' and 'DisplayOrder' options.

  • This option only works with categorical data.

Data

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Type of normalization, specified as one of the values in this table. For each bin i:

  • vi is the bin value.

  • ci is the number of elements in the bin.

  • wi is the width of the bin.

  • N is the number of elements in the input data. This value can be greater than the binned data if the data contains missing values, such as NaN, or if some of the data lies outside the bin limits.

ValueBin ValuesNotes
'count' (default)

vi=ci

  • Count or frequency of observations.

  • Sum of bin values is at most numel(X), or sum(ismember(X(:),Categories)) for categorical data. The sum is less than this only when some of the input data is not included in the bins.

'probability'

vi=ciN

  • Relative probability.

  • The number of elements in each bin relative to the total number of elements in the input data is at most 1.

'percentage'

vi=100*ciN

  • Relative percentage.

  • 'percentage' does not support categorical data.

  • The percentage of elements in each bin is at most 100.

'countdensity'

vi=ciwi

  • Count or frequency scaled by width of bin.

  • For categorical data, this is the same as 'count'.

  • 'countdensity' does not support datetime or duration data.

  • The sum of the bin areas is at most numel(X).

'cumcount'

vi=j=1icj

  • Cumulative count, or the number of observations in each bin and all previous bins.

  • N(end) is at most numel(X), or sum(ismember(X(:),Categories)) for categorical data.

'pdf'

vi=ciNwi

  • Probability density function estimate.

  • For categorical data, this is the same as 'probability'.

  • 'pdf' does not support datetime or duration data.

  • The sum of the bin areas is at most 1.

'cdf'

vi=j=1icjN

  • Cumulative distribution function estimate.

  • The count of each bin is equal to the cumulative relative number of observations in the bin and all previous bins.

  • N(end) is at most 1.

Example: histogram(X,'Normalization','pdf') bins the data using an estimate of the probability density function.

Color and Styling

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Histogram display style, specified as either 'bar' or 'stairs'.

  • 'bar' — Display a histogram bar plot. over each window of A. This method is useful for reducing periodic trends in data.

  • 'stairs' — Display a stairstep plot, which displays the outline of the histogram without filling the interior.

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

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

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

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 BarWidth is 1, then adjacent bars touch.

This option only works with categorical data.

Example: 0.5

Histogram bar color, specified as one of these values:

  • 'none' — Bars are not filled.

  • 'auto' — Histogram bar color is chosen automatically (default).

  • RGB triplet, hexadecimal color code, or color name — Bars are filled with the specified color.

    RGB triplets and hexadecimal color codes are useful for specifying custom colors.

    • 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].

    • A hexadecimal color code is a character vector or a string scalar that starts with a hash symbol (#) followed by three or six hexadecimal digits, which can range from 0 to F. The values are not case sensitive. Thus, the color codes "#FF8800", "#ff8800", "#F80", and "#f80" are equivalent.

    Alternatively, you can specify some common colors by name. This table lists the named color options, the equivalent RGB triplets, and hexadecimal color codes.

    Color NameShort NameRGB TripletHexadecimal Color CodeAppearance
    "red""r"[1 0 0]"#FF0000"

    Sample of the color red

    "green""g"[0 1 0]"#00FF00"

    Sample of the color green

    "blue""b"[0 0 1]"#0000FF"

    Sample of the color blue

    "cyan" "c"[0 1 1]"#00FFFF"

    Sample of the color cyan

    "magenta""m"[1 0 1]"#FF00FF"

    Sample of the color magenta

    "yellow""y"[1 1 0]"#FFFF00"

    Sample of the color yellow

    "black""k"[0 0 0]"#000000"

    Sample of the color black

    "white""w"[1 1 1]"#FFFFFF"

    Sample of the color white

    Here are the RGB triplets and hexadecimal color codes for the default colors MATLAB® uses in many types of plots.

    RGB TripletHexadecimal Color CodeAppearance
    [0 0.4470 0.7410]"#0072BD"

    Sample of RGB triplet [0 0.4470 0.7410], which appears as dark blue

    [0.8500 0.3250 0.0980]"#D95319"

    Sample of RGB triplet [0.8500 0.3250 0.0980], which appears as dark orange

    [0.9290 0.6940 0.1250]"#EDB120"

    Sample of RGB triplet [0.9290 0.6940 0.1250], which appears as dark yellow

    [0.4940 0.1840 0.5560]"#7E2F8E"

    Sample of RGB triplet [0.4940 0.1840 0.5560], which appears as dark purple

    [0.4660 0.6740 0.1880]"#77AC30"

    Sample of RGB triplet [0.4660 0.6740 0.1880], which appears as medium green

    [0.3010 0.7450 0.9330]"#4DBEEE"

    Sample of RGB triplet [0.3010 0.7450 0.9330], which appears as light blue

    [0.6350 0.0780 0.1840]"#A2142F"

    Sample of RGB triplet [0.6350 0.0780 0.1840], which appears as dark red

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.

Histogram edge color, specified as one of these values:

  • 'none' — Edges are not drawn.

  • 'auto' — Color of each edge is chosen automatically.

  • RGB triplet, hexadecimal color code, or color name — Edges use the specified color.

    RGB triplets and hexadecimal color codes are useful for specifying custom colors.

    • 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].

    • A hexadecimal color code is a character vector or a string scalar that starts with a hash symbol (#) followed by three or six hexadecimal digits, which can range from 0 to F. The values are not case sensitive. Thus, the color codes "#FF8800", "#ff8800", "#F80", and "#f80" are equivalent.

    Alternatively, you can specify some common colors by name. This table lists the named color options, the equivalent RGB triplets, and hexadecimal color codes.

    Color NameShort NameRGB TripletHexadecimal Color CodeAppearance
    "red""r"[1 0 0]"#FF0000"

    Sample of the color red

    "green""g"[0 1 0]"#00FF00"

    Sample of the color green

    "blue""b"[0 0 1]"#0000FF"

    Sample of the color blue

    "cyan" "c"[0 1 1]"#00FFFF"

    Sample of the color cyan

    "magenta""m"[1 0 1]"#FF00FF"

    Sample of the color magenta

    "yellow""y"[1 1 0]"#FFFF00"

    Sample of the color yellow

    "black""k"[0 0 0]"#000000"

    Sample of the color black

    "white""w"[1 1 1]"#FFFFFF"

    Sample of the color white

    Here are the RGB triplets and hexadecimal color codes for the default colors MATLAB uses in many types of plots.

    RGB TripletHexadecimal Color CodeAppearance
    [0 0.4470 0.7410]"#0072BD"

    Sample of RGB triplet [0 0.4470 0.7410], which appears as dark blue

    [0.8500 0.3250 0.0980]"#D95319"

    Sample of RGB triplet [0.8500 0.3250 0.0980], which appears as dark orange

    [0.9290 0.6940 0.1250]"#EDB120"

    Sample of RGB triplet [0.9290 0.6940 0.1250], which appears as dark yellow

    [0.4940 0.1840 0.5560]"#7E2F8E"

    Sample of RGB triplet [0.4940 0.1840 0.5560], which appears as dark purple

    [0.4660 0.6740 0.1880]"#77AC30"

    Sample of RGB triplet [0.4660 0.6740 0.1880], which appears as medium green

    [0.3010 0.7450 0.9330]"#4DBEEE"

    Sample of RGB triplet [0.3010 0.7450 0.9330], which appears as light blue

    [0.6350 0.0780 0.1840]"#A2142F"

    Sample of RGB triplet [0.6350 0.0780 0.1840], which appears as dark red

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

Transparency of histogram bars, specified as a scalar value in range [0,1]. 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.

Transparency of histogram bar edges, specified as a scalar value in the range [0,1]. 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.

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

Line StyleDescriptionResulting Line
"-"Solid line

Sample of solid line

"--"Dashed line

Sample of dashed line

":"Dotted line

Sample of dotted line

"-."Dash-dotted line

Sample of dash-dotted line, with alternating dashes and dots

"none"No lineNo line

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

Example: 1.5

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

Output Arguments

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

Properties

Histogram PropertiesHistogram appearance and behavior

Object Functions

morebinsIncrease number of histogram bins
fewerbinsDecrease number of histogram bins

Examples

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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)

Figure contains an axes object. The axes object contains an object of type histogram.

h = 
  Histogram with properties:

             Data: [10000x1 double]
           Values: [2 2 1 6 7 17 29 57 86 133 193 271 331 421 540 613 730 748 776 806 824 721 623 503 446 326 234 191 132 78 65 33 26 11 8 5 5]
          NumBins: 37
         BinEdges: [-3.8000 -3.6000 -3.4000 -3.2000 -3 -2.8000 -2.6000 -2.4000 -2.2000 -2 -1.8000 -1.6000 -1.4000 -1.2000 -1 -0.8000 -0.6000 -0.4000 -0.2000 0 0.2000 0.4000 0.6000 0.8000 1.0000 1.2000 1.4000 1.6000 1.8000 2.0000 2.2000 ... ] (1x38 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

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

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

Figure contains an axes object. The axes object contains an object of type histogram.

h = 
  Histogram with properties:

             Data: [1000x1 double]
           Values: [1 3 0 6 14 19 31 54 74 80 92 122 104 115 88 80 38 32 21 9 5 5 5 0 2]
          NumBins: 25
         BinEdges: [-3.4000 -3.1200 -2.8400 -2.5600 -2.2800 -2 -1.7200 -1.4400 -1.1600 -0.8800 -0.6000 -0.3200 -0.0400 0.2400 0.5200 0.8000 1.0800 1.3600 1.6400 1.9200 2.2000 2.4800 2.7600 3.0400 3.3200 3.6000]
         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 = 1×25

     1     3     0     6    14    19    31    54    74    80    92   122   104   115    88    80    38    32    21     9     5     5     5     0     2

Generate 1,000 random numbers and create a histogram.

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

Figure contains an axes object. The axes object contains an object of type histogram.

h = 
  Histogram with properties:

             Data: [1000x1 double]
           Values: [3 1 2 15 17 27 53 79 85 101 127 110 124 95 67 32 27 16 6 6 4 1 2]
          NumBins: 23
         BinEdges: [-3.3000 -3.0000 -2.7000 -2.4000 -2.1000 -1.8000 -1.5000 -1.2000 -0.9000 -0.6000 -0.3000 0 0.3000 0.6000 0.9000 1.2000 1.5000 1.8000 2.1000 2.4000 2.7000 3 3.3000 3.6000]
         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)

Figure contains an axes object. The axes object contains an object of type histogram.

Nbins = 29

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

h.NumBins = 31;

Figure contains an axes object. The axes object contains an object of type 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 |x|<2. 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);

Figure contains an axes object. The axes object contains an object of type histogram.

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';

Figure contains an axes object. The axes object contains an object of type 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 = 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 

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

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

Figure contains an axes object. The axes object contains an object of type categoricalhistogram.

h = 
  Histogram with properties:

              Data: [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]
            Values: [11 14 2]
    NumDisplayBins: 3
        Categories: {'yes'  'no'  'undecided'}
      DisplayOrder: 'data'
     Normalization: 'count'
      DisplayStyle: 'bar'
         FaceColor: 'auto'
         EdgeColor: [0 0 0]

  Use GET to show all properties

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

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

Figure contains an axes object. The axes object contains an object of type histogram.

h = 
  Histogram with properties:

             Data: [1000x1 double]
           Values: [0.0030 1.0000e-03 0.0020 0.0150 0.0170 0.0270 0.0530 0.0790 0.0850 0.1010 0.1270 0.1100 0.1240 0.0950 0.0670 0.0320 0.0270 0.0160 0.0060 0.0060 0.0040 1.0000e-03 0.0020]
          NumBins: 23
         BinEdges: [-3.3000 -3.0000 -2.7000 -2.4000 -2.1000 -1.8000 -1.5000 -1.2000 -0.9000 -0.6000 -0.3000 0 0.3000 0.6000 0.9000 1.2000 1.5000 1.8000 2.1000 2.4000 2.7000 3 3.3000 3.6000]
         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

Generate 100,000 normally distributed random numbers. Use a standard deviation of 15 and a mean of 100.

x = 100 + 15*randn(1e5,1);

Plot a histogram of the random numbers. Scale and label the y-axis as percentages.

edges = 55:15:145;
histogram(x,edges,Normalization="percentage")
ytickformat("percentage")

Figure contains an axes object. The axes object contains an object of type histogram.

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);

Figure contains an axes object. The axes object contains 2 objects of type histogram.

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;

Figure contains an axes object. The axes object contains 2 objects of type histogram.

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: [3 1 2 15 17 27 53 79 85 101 127 110 124 95 67 32 27 16 6 6 4 1 2]
          NumBins: 23
         BinEdges: [-3.3000 -3.0000 -2.7000 -2.4000 -2.1000 -1.8000 -1.5000 -1.2000 -0.9000 -0.6000 -0.3000 0 0.3000 0.6000 0.9000 1.2000 1.5000 1.8000 2.1000 2.4000 2.7000 3 3.3000 3.6000]
         BinWidth: 0.3000
        BinLimits: [-3.3000 3.6000]
    Normalization: 'count'
        FaceColor: 'auto'
        EdgeColor: [0 0 0]

  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';

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')

Figure contains an axes object. The axes object contains an object of type histogram.

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 μ, standard deviation σ, and variance σ2 is

f(x,μ,σ)=1σ2π exp[-(x-μ)22σ2].

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)

Figure contains an axes object. The axes object contains 2 objects of type histogram, line.

Use the savefig function to save a histogram figure.

histogram(randn(10));
savefig('histogram.fig');
close gcf

Use openfig to load the histogram figure back into MATLAB®. openfig also returns a handle to the figure, h.

h = openfig('histogram.fig');

Figure contains an axes object. The axes object contains an object of type histogram.

Use the findobj function to locate the correct object handle from the figure handle. This allows you to continue manipulating the original histogram object used to generate the figure.

y = findobj(h,'type','histogram')
y = 
  Histogram with properties:

             Data: [10x10 double]
           Values: [2 17 28 32 16 3 2]
          NumBins: 7
         BinEdges: [-3 -2 -1 0 1 2 3 4]
         BinWidth: 1
        BinLimits: [-3 4]
    Normalization: 'count'
        FaceColor: 'auto'
        EdgeColor: [0 0 0]

  Use GET to show all properties

Tips

  • Histogram plots created using histogram have a context menu in plot edit mode that enables interactive manipulations in the figure window. For example, you can use the context menu to interactively change the number of bins, align multiple histograms, or change the display order.

  • When you add data tips to a histogram plot, they display the bin edges and bin count.

Extended Capabilities

Version History

Introduced in R2014b

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