_hist_bin_auto(x, range)
The FD estimator is usually the most robust method, but its width estimate tends to be too large for small x and bad for data with limited variance. The Sturges estimator is quite good for small (<1000) datasets and is the default in the R language. This method gives good off-the-shelf behaviour.
If there is limited variance the IQR can be 0, which results in the FD bin width being 0 too. This is not a valid bin width, so np.histogram_bin_edges
chooses 1 bin instead, which may not be optimal. If the IQR is 0, it's unlikely any variance-based estimators will be of use, so we revert to the Sturges estimator, which only uses the size of the dataset in its calculation.
Input data that is to be histogrammed, trimmed to range. May not be empty.
Histogram bin estimator that uses the minimum width of the Freedman-Diaconis and Sturges estimators if the FD bin width is non-zero. If the bin width from the FD estimator is 0, the Sturges estimator is used.
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