histogram

scikitplot.cexternals._astropy.stats.histogram(a, bins=10, range=None, weights=None, **kwargs)

Enhanced histogram function, providing adaptive binnings.

This is a histogram function that enables the use of more sophisticated algorithms for determining bins. Aside from the bins argument allowing a string specified how bins are computed, the parameters are the same as numpy.histogram.

Parameters:

aarray-like

array of data to be histogrammed

binsint, list, or str, optional

If bins is a string, then it must be one of:

• ‘blocks’ : use bayesian blocks for dynamic bin widths

• ‘knuth’ : use Knuth’s rule to determine bins

• ‘scott’ : use Scott’s rule to determine bins

• ‘freedman’ : use the Freedman-Diaconis rule to determine bins

rangetuple or None, optional

the minimum and maximum range for the histogram. If not specified, it will be (x.min(), x.max())

weightsarray-like, optional

An array the same shape as a. If given, the histogram accumulates the value of the weight corresponding to a instead of returning the count of values. This argument does not affect determination of bin edges.

**kwargsdict, optional

Extra arguments are described in numpy.histogram.

Returns:

histarray

The values of the histogram. See density and weights for a description of the possible semantics.

bin_edgesarray of dtype float

Return the bin edges (length(hist)+1).

Parameters:

• a (_Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | complex | bytes | str | _NestedSequence[complex | bytes | str])

• bins (int | list[float] | Literal['blocks', 'knuth', 'scott', 'freedman'] | None)

• range (tuple[float, float] | None)

• weights (_Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | complex | bytes | str | _NestedSequence[complex | bytes | str] | None)

Return type:

tuple[ndarray[tuple[Any, …], dtype[_ScalarT]], ndarray[tuple[Any, …], dtype[_ScalarT]]]

See also

numpy.histogram