ENH: histogram_bin_edges: add an equal-frequency (quantile) binning strategy

Proposed new feature or change:

np.histogram_bin_edges(x, bins=...) supports 'auto', 'fd', 'doane', 'scott', 'stone', 'rice', 'sturges', 'sqrt' - all equal-width strategies (they pick a bin width/count, then space edges evenly across the data's range). There's no built-in equal-frequency strategy (edges placed so each bin holds roughly the same number of observations), even though it's one of the two textbook-standard binning approaches and is what you reach for whenever the data is skewed and equal-width bins would leave most of the mass in one or two bins - a genuinely common histogram to want, not a niche one.

Today it has to be hand-rolled: np.unique(np.quantile(x, np.linspace(0, 1, n_bins + 1)))

Describe the solution you'd like:

np.histogram_bin_edges(x, bins='quantile') (naming open to bikeshedding - 'equal_frequency' also reads clearly), using the number of bins from a paired range/bins=(strategy, n) argument or a sensible default, with np.unique applied to the resulting edges so duplicate quantiles collapse into fewer, wider bins rather than erroring or producing zero-width bins.

Describe alternatives you've considered:

pandas.qcut does the equal-frequency binning itself, but returns pandas-specific Categorical bin labels tied to a Series, not raw histogram bin edges compatible with np.histogram/plt.hist - a different-shaped output for a different purpose (labeling data by bin vs. building a histogram). There's no numpy-native way to get equal-frequency edges as plain floats.

Additional context:

Happy to submit a PR - the core computation is a few lines (see the "solution" section above), the main design decision is naming and how to specify bin count for this strategy consistently with the existing string-strategy API.