I spent an hour today trying to figure out why

return abs(val-desired) <= 0.1

was occasionally returning False, despite val and desired having an absolute difference of <=0.1. After some debugging, I found out that -13.2 + 13.3 = 0.10000000000000142. Now I understand that CPUs cannot easily represent most real numbers, but this is an exception, because you can subtract 0.00000000000000142 and get 0.1, so it can be represented in Python.

I am running Python 2.7 on Intel Core architecture CPUs (this is all I have been able to test it on). I'm curious to know how I can store a value of 0.1 despite not being able to apply arithmetic to particular floating point values. val and desired are float values.

Yes, this can be a bit surprising:

>>> +13.3
13.300000000000001
>>> -13.2
-13.199999999999999
>>> 0.1
0.10000000000000001

All these numbers can be represented with some 16 digits of accuracy. So why:

>>> 13.3-13.2
0.10000000000000142

Why only 14 digits of accuracy in that case?

Well, that's because 13.3 and -13.2 have 16 digits of accuracy, which means 14 decimal points, since there are two digits before the decimal point. So the result also have 14 decimal points of accuracy. Even though the computer can represent numbers with 16 digits.

If we make the numbers bigger, the accuracy of the result decreases further:

>>> 13000.3-13000.2
0.099999999998544808>>> 1.33E10-13.2E10
-118700000000.0

In short, the accuracy of the result depends on the accuracy of the input.