Excluding rightmost edge in numpy.histogram

Question

I have a list of numbers a and a list of bins which I shall use to bin the numbers in a using numpy.histogram . the bins are calculated from the mean and standard deviation ( std ) of a . So the number of bins is B , and the minimum value of the first bin is mean - std , the maximum of the last bin being mean + std . (The text in bold indicates my final goal)

An example goes like the following:

>>> a
array([1, 1, 3, 2, 2, 6])

>>> bins = np.linspace(mean - std, mean + std, B + 1)
array([ 0.79217487,  1.93072496,  3.06927504,  4.20782513]))

>>> numpy.histogram(a, bins = bins)[0]
(array([2, 3, 0], dtype=int32)

However, I want to exclude the rightmost edge of the last bin - ie if some value in a exactly equals mean + std , I do not wish to include it in the last bin. The caricature about mean and std is not important, excluding the rightmost edge (aka making it a half-open interval) is. The doc says, unfortunately in this regard:

All but the last (righthand-most) bin is half-open. In other words, if bins is:

[1, 2, 3, 4] then the first bin is [1, 2) (including 1, but excluding 2) and the second [2, 3). The last bin, however, is [3, 4], which includes 4.

Is there a simple solution I can employ? That is, one that does not involve manually fixing edges. That is something I can do, but that's not what I'm looking for. Is there a flag I can pass or a different method I can use?

Answer 1

Here's one (kind of crude?) way to turn the make the last bin half-open instead of closed. What I'm doing is subtracting the smallest possible value from the right side of the right-most bin:

a = np.array([1, 1, 3, 2, 2, 6])
B = 3 # (in this example) 
bins = np.linspace(a.mean() - a.std(), a.mean() + a.std(), B + 1)
# array([ 0.79217487,  1.93072496,  3.06927504,  4.20782513]))
bins[-1] -= np.finfo(float).eps # <== this is the crucial line
np.histogram(a, bins = bins)

If you're using some other type other than float for the values in a , using a different type in the call to finfo . For example:

np.finfo(float).eps
np.finfo(np.float128).eps