Numpy has three histogram functions - histogram, histogram2d, and

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

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.]) 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


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