Numpy has three histogram functions - histogram, histogram2d, and histogramdd. histogram is by far the most widely used, and in the absence of weights and normalization, returns an np.intp count for each bin. histogramdd (for which histogram2d is a wrapper) returns np.float64 in all circumstances. As a contrived comparison
x = np.linspace(0, 1)>>> h, e = np.histogram(x*x, bins=4); h array([25, 10, 8, 7], dtype=int64)>>> h, e = np.histogramdd((x*x,), bins=4); h array([25., 10., 8., 7.])
https://github.com/numpy/numpy/issues/7845 tracks this inconsistency. The fix is now trivial: the question is, will changing the return type break people’s code? Either we should: 1. Just change it, and hope no one is broken by it 2. Add a dtype argument: - If dtype=None, behave like np.histogram - If dtype is not specified, emit a future warning recommending to use dtype=None or dtype=float - In future, change the default to None 3. Create a new better-named function histogram_nd, which can also be created without the mistake that is https://github.com/numpy/numpy/issues/10864. Thoughts? Eric