numpy.arange stop precision issue

All elements of the array generated by numpy.arange(start, stop, step) should be strictly smaller than stop.

On my Ubuntu 14.04 installation this happened:

In [117]: np.arange(0.5, 0.8, 0.1)
Out[117]: array([ 0.5,  0.6,  0.7,  0.8])

In [118]: np.arange(0.5, 0.9, 0.1)
Out[118]: array([ 0.5,  0.6,  0.7,  0.8])

In [123]: np.arange(0.5, 0.8, 0.1)[-1]
Out[123]: 0.79999999999999993

In [124]: np.arange(0.5, 0.9, 0.1)[-1]
Out[124]: 0.79999999999999993

Is this an issue on other installations?

[jonas-eschle]

@jaimefrio: I would definitely vote to reopen this issue. It is impossible that the function depends on the implementation and some rounding luck! Or let’s put it this way: Numpy is supposed to be a “mathematical-operations”-library. If a function then is supposed to return a list from 0.5 to 0.8 excluding 0.8 and with stepsize 0.1, it has to return a list with exactly three components, [0.5, 0.6, 0.7] (yes, actually [0.50000001, 0.6, 0.69999993] or similar, but definitely not an additional number]). I agree, that rounding errors can make life more difficult when doing calculations, but ignoring them actively and basically saying “deal with the problem yourself” is definitely not the solution. Instead, arange has to recognize rounding errors, otherwise, the function is not usable (search the web, a lot of people complain and work around this error).

A possible implementation could have an additional argument, something like “stopping_precision”, that defines the “stopping-range”.

I love numpy and I would really like to see arange to be usable again. Or, honestly, how many functions do you regularly use whose return depends on… luck? And on the other hand, if you call it a “feature”: please name one single use-case for this “feature” 😉

[matthew-brett]

Floating point numbers are “never” exact.

I think you mean hardly ever exact.