Okay so I get that some numbers can't be represented properly in binary just like 1/3 can't be fully represented in decimal.

So how e when I console.log(0.3) it returns 0.3 but when I console.log(0.1 + 0.2) it returns the 0.30000000000000004

How e it is accounting for the error (if it even is) when simply outputting 0.3 but doesn't when the addition occurs?

Okay so I get that some numbers can't be represented properly in binary just like 1/3 can't be fully represented in decimal.

So how e when I console.log(0.3) it returns 0.3 but when I console.log(0.1 + 0.2) it returns the 0.30000000000000004

How e it is accounting for the error (if it even is) when simply outputting 0.3 but doesn't when the addition occurs?

• javascript
• math
• floating-point-precision

Share Improve this question Follow edited Jan 31, 2014 at 21:38 Wooble 90.1k1212 gold badges110110 silver badges132132 bronze badges asked Jan 31, 2014 at 21:36 robjtederobjtede 74655 silver badges1717 bronze badges 5

• Removed [python] tag because this is specific to JavaScript's display of numbers. – Wooble Commented Jan 31, 2014 at 21:38
• 1 @Wooble Actually Python exhibits the same behavior, even with a somewhat-recent change that hides such small errors in the display of many floats. – user395760 Commented Jan 31, 2014 at 21:40
• Well, repr does. str does not. – Wooble Commented Jan 31, 2014 at 21:46
• 1 To see on your own what you've been answered, try to force the system to represent the numbers with higher precision than the default one. E.g, try (in Python) {:.20f}".format(0.1) – Ricardo Cárdenes Commented Jan 31, 2014 at 21:50
• @Wooble In recent versions both do because both use the new algorithm. – user395760 Commented Jan 31, 2014 at 23:14

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4 Answers 4

Sorted by: Reset to default 7

Suppose we approximate 1/3 and 2/3 in decimal.

1/3 = 0.333
2/3 = 0.667

and we add 1/3+1/3:

1/3+1/3 = 0.333 + 0.333 = 0.666

We didn't get our approximation of 2/3. Rounding 1/3 to something we can represent in decimal didn't produce a number equal to half of what we got when we rounded 2/3.

The same thing happens in binary. We round 0.1 and 0.2 to numbers we can represent in binary, but the sum of the approximations is slightly different from what we get if we approximate 0.3. We get something a bit higher, and the result is displayed as 0.30000000000000004.

The inaccuracies in the internal representations of 0.1 and 0.2 are small enough that the Javascript printer ignores them when it's printing these numbers. But when you add them together the inaccuracies accumulate, and this is enough to show up when the result is printed.

The way Java prints floating-point numbers is a significant part of the behavior you are seeing: By default, Java does not print the exact value of a floating-point number. It prints just enough digits to precisely identify the double that is being printed.

Thus, if you set x to .3, x is actually set to 0.299999999999999988897769753748434595763683319091796875. When Java prints this, it prints only “.3”, because converting “.3” to double yields the value of x, 0.299999999999999988897769753748434595763683319091796875.

When you use .1 and .2, these are actually the values 0.1000000000000000055511151231257827021181583404541015625 and 0.200000000000000011102230246251565404236316680908203125. When you add them (in double format), the result is 0.3000000000000000444089209850062616169452667236328125.

When you print this value, Java prints “0.30000000000000004” because it needs to show all those digits in order to produce a numeral that, when converted back to double, will produce 0.3000000000000000444089209850062616169452667236328125.

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Here is the documentation for how double values are printed. It says:

How many digits must be printed for the fractional part…? There must be at least one digit to represent the fractional part, and beyond that as many, but only as many, more digits as are needed to uniquely distinguish the argument value from adjacent values of type double.

As you said, in base 10 you can't accurately describe the fraction 1/3. Similarly, in base 2, you can't accurately describe certain other fractions. So, when the puter adds 0.1 and 0.2, it's actually adding something like 0.10000000001 and 0.20000000003.