# scott_bin_width¶

astropy.stats.scott_bin_width(data, return_bins=False)[source]

Return the optimal histogram bin width using Scott’s rule

Scott’s rule is a normal reference rule: it minimizes the integrated mean squared error in the bin approximation under the assumption that the data is approximately Gaussian.

Parameters: data : array-like, ndim=1 observed (one-dimensional) data return_bins : bool (optional) if True, then return the bin edges width : float optimal bin width using Scott’s rule bins : ndarray bin edges: returned if return_bins is True

Notes

The optimal bin width is

$\Delta_b = \frac{3.5\sigma}{n^{1/3}}$

where $$\sigma$$ is the standard deviation of the data, and $$n$$ is the number of data points [1].

References

 [1] (1, 2) Scott, David W. (1979). “On optimal and data-based histograms”. Biometricka 66 (3): 605-610