[Numpy-discussion] np.histogram: upper range bin

[Christopher Barker]

Peter Butterworth wrote:
> in np.histogram the top-most bin edge is inclusive of the upper range
> limit. As documented in the docstring (see below) this is actually the
> expected behavior, but this can lead to some weird enough results:
> 
> In [72]: x=[1, 2, 3, 4]; np.histogram(x, bins=3)
> Out[72]: (array([1, 1, 2]), array([ 1.,  2.,  3., 4.]))
> 
> Is there any way round this or an alternative implementation without
> this issue ?

The way around it is what you've identified -- making sure your bins are 
right. But I think the current behavior is the way it "should" be. It 
keeps folks from inadvertently loosing stuff off the end -- the lower 
end is inclusive, so the upper end should be too. In the middle bins, 
one has to make an arbitrary cut-off, and put the values on the "line" 
somewhere.


One thing to keep in mind is that, in general, histogram is designed for 
floating point numbers, not just integers -- counting integers can be 
accomplished other ways, if that's what you really want (see 
np.bincount). But back to your example:

 > In [72]: x=[1, 2, 3, 4]; np.histogram(x, bins=3)

Why do you want only 3 bins here? using 4 gives you what you want. If 
you want more control, then it seems you really want to know how many of 
each of the values 1,2,3,4 there are. so you want 4 bins, each 
*centered* on the integers, so you might do:

In [8]: np.histogram(x, bins=4, range=(0.5, 4.5))
Out[8]: (array([1, 1, 1, 1]), array([ 0.5,  1.5,  2.5,  3.5,  4.5]))

or, if you want to be more explicit:

In [14]: np.histogram(x, bins=np.linspace(0.5, 4.5, 5))
Out[14]: (array([1, 1, 1, 1]), array([ 0.5,  1.5,  2.5,  3.5,  4.5]))


HTH,

-Chris




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