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Histogram bin counts

```
[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)`

`[`

partitions the `N`

,`edges`

]
= histcounts(`X`

)`X`

values
into bins, and returns the count in each bin, as well as the bin edges.
The `histcounts`

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.

counts
only the elements in `N`

= histcounts(`C`

,`Categories`

)`C`

whose value is equal to
the subset of categories specified by `Categories`

.

`[`

also returns the categories
that correspond to each count in `N`

,`Categories`

]
= histcounts(___)`N`

using either
of the previous syntaxes for categorical arrays.

`[___] = histcounts(___,`

uses
additional options specified by one or more `Name,Value`

)`Name,Value`

pair
arguments using any of the input or output argument combinations in
previous syntaxes. For example, you can specify `'BinWidth'`

and
a scalar to adjust the width of the bins for numeric data. For categorical
data, you can specify `'Normalization'`

and either `'count'`

, `'countdensity'`

, `'probability'`

, `'pdf'`

, `'cumcount'`

,
or `'cdf'`

.

The behavior of

`histcounts`

is similar to that of the`discretize`

function. Use`histcounts`

to find the number of elements in each bin. On the other hand, use`discretize`

to find which bin each element belongs to (without counting).