On Thu, 26 Jun 2014 13:39:23 +1000, Ben Finney wrote: > Steven D'Aprano <st...@pearwood.info> writes: > >> On Wed, 25 Jun 2014 14:12:31 -0700, Maciej Dziardziel wrote: >> >> > Floating points values use finite amount of memory, and cannot >> > accurately represent infinite amount of numbers, they are only >> > approximations. This is limitation of float type and applies to any >> > languages that uses types supported directly by cpu. To deal with it >> > you can either use decimal.Decimal type that operates using decimal >> > system and saves you from such surprises >> >> That's a myth. decimal.Decimal *is* a floating point value > > That's misleading: Decimal uses *a* floating-point representation, but > not the one commonly referred to. That is, Decimal does not use IEEE-754 > floating point.

You're technically correct, but only by accident. IEEE-754 covers both binary and decimal floating point numbers: http://en.wikipedia.org/wiki/IEEE_floating_point but Python's decimal module is based on IEEE-854, not 754. http://en.wikipedia.org/wiki/IEEE_854-1987 So you're right on a technicality, but wrong in the sense of knowing what you're talking about *wink* >> and is subject to *exactly* the same surprises as binary floats, > > Since those “surprises” are the ones inherent to *decimal*, not binary, > floating point, I'd say it's also misleading to refer to them as > “exactly the same surprises”. They're barely surprises at all, to > someone raised on decimal notation. Not at all. They are surprises to people who are used to *mathematics*, fractions, rational numbers, the real numbers, etc. It is surprising that the rational number "one third" added together three times should fail to equal one. Ironically, binary float gets this one right: py> 1/3 + 1/3 + 1/3 == 1 True py> Decimal(1)/3 + Decimal(1)/3 + Decimal(1)/3 == 1 False but for other rationals, that is not necessarily the case. It is surprising when x*(y+z) fails to equal x*y + x*z, but that can occur with both binary floats and Decimals. It is surprising when (x + y) + z fails to equal x + (y + z), but that can occur with both binary floats and Decimals. It is surprising when x != 0 and y != 0 but x*y == 0, but that too can occur with both binary floats and Decimals. And likewise for most other properties of the rationals and reals, which people learn in school, or come to intuitively expect. People are surprised when floating-point arithmetic fails to obey the rules of mathematical arithmetic. If anyone is aware of a category of surprise which binary floats are prone to, but Decimal floats are not, apart from the decimal- representation issue I've already mentioned, I'd love to hear of it. But I doubt such a thing exists. Decimal in the Python standard library has another advantage, it supports user-configurable precisions. But that doesn't avoid any category of surprise, it just mitigates against being surprised as often. > This makes the Decimal functionality starkly different from the built-in > ‘float’ type, and it *does* save you from the rather-more-surprising > behaviour of the ‘float’ type. This is not mythical. It simply is not true that Decimal avoids the floating point issues that "What Every Computer Scientist Needs To Know About Floating Point" warns about: http://docs.oracle.com/cd/E19957-01/806-3568/ncg_goldberg.html It *cannot* avoid them, because Decimal is itself a floating point format, it is not an infinite precision number type like fractions.Fraction. Since Decimal cannot avoid these issues, all we can do is push the surprises around, and hope to have less of them, or shift them to parts of the calculation we don't care about. (Good luck with that.) Decimal, by default, uses 28 decimal digits of precision, about 11 or 12 more digits than Python floats are able to provide. So right away, by shifting to Decimal you gain precision and hence might expect fewer surprises, all else being equal. But all else isn't equal. The larger the base, the larger the "wobble". See Goldberg above for the definition of wobble, but it's a bad thing. Binary floats have the smallest wobble, which is to their advantage. If you stick to trivial calculations using nothing but trivially "neat" decimal numbers, like 0.1, you may never notice that Decimal is subject to the same problems as float (only worse, in some ways -- Decimal calculations can fail in some spectacularly horrid ways that binary floats cannot). But as soon as you start doing arbitrary calculations, particularly if they involve divisions and square roots, things are no longer as neat and tidy. Here's an error that *cannot* occur with binary floats: the average of two numbers x and y is not guaranteed to lie between x and y! py> from decimal import * py> getcontext().prec = 3 py> x = Decimal('0.516') py> y = Decimal('0.518') py> (x + y) / 2 Decimal('0.515') Ouch! -- Steven -- https://mail.python.org/mailman/listinfo/python-list