scott_bin_width#
- astropy.stats.scott_bin_width(data: ArrayLike, return_bins: bool | None = False) float | tuple[float, NDArray] [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:
- dataarray_like, ndim=1
observed (one-dimensional) data
- return_binsbool, optional
if True, then return the bin edges
- Returns:
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]Scott, David W. (1979). “On optimal and data-based histograms”. Biometricka 66 (3): 605-610