numpy.arange makes no sense at all. How does it work?

Question

As you can see here, np.arange returns an array with the values from the first to the last (But no the last!) with a separation you choose.

My examples:

In[600]: np.arange(1,11,1)
Out[600]: array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])


In [602]: np.arange(1,4,0.2)
Out[602]: array([ 1. ,  1.2,  1.4,  1.6,  1.8,  2. ,  2.2,  2.4,  2.6,  2.8,  3. ,
    3.2,  3.4,  3.6,  3.8])


In [604]: np.arange(3.24,3.6,0.04)
Out[604]: array([ 3.24,  3.28,  3.32,  3.36,  3.4 ,  3.44,  3.48,  3.52,  3.56])

But:

In [605]: np.arange(14.04, 14.84, 0.08)
Out[605]: array([ 14.04,  14.12,  14.2 ,  14.28,  14.36,  14.44,  14.52,  14.6 ,
    14.68,  14.76,  14.84])

What is wrong with this last one? Why does it returns 14.84 instead of stopping at 14.76?

I have been observing that it happens sometimes, but im not able to see a patron or so. What´s going on?

Note also that they recognize it in the documentation: "When using a non-integer step, such as 0.1, the results will often not be consistent. It is better to use linspace for these cases." — lib, Nov 21 '15 at 16:58

score 2 · Accepted Answer · edited May 23 '17 at 11:44

It's floating point precision, e.g. see this question: python numpy arange unexpected results

You could get around it like so:

>>> np.arange(14.04, 14.839, 0.08)
array([ 14.04, 14.12, 14.2, 14.28, 14.36, 14.44, 14.52, 14.6, 14.68, 14.76])

...but this is pretty hacky. It's usually a better idea to use numpy.linspace(), but then you have to give the number of steps, not the interval, so it can be a bit of a pain.

>>> np.linspace(14.04, 14.76, 9)
array([ 14.04,  14.13,  14.22,  14.31, 14.4, 14.49,  14.58, 14.67, 14.76])

Choose your poison!

numpy.arange makes no sense at all. How does it work?

1 Answers1