I know that most decimals don't have an exact floating point representation (Is floating point math broken?).
But I don't see why
>>> from decimal import Decimal
The simple answer is because
3*0.1 != 0.3 due to quantization (roundoff) error (whereas
4*0.1 == 0.4 because multiplying by a power of two is usually an "exact" operation).
You can use the
.hex method in Python to view the internal representation of a number (basically, the exact binary floating point value, rather than the base-10 approximation). This can help to explain what's going on under the hood.
>>> (0.1).hex() '0x1.999999999999ap-4' >>> (0.3).hex() '0x1.3333333333333p-2' >>> (0.1*3).hex() '0x1.3333333333334p-2' >>> (0.4).hex() '0x1.999999999999ap-2' >>> (0.1*4).hex() '0x1.999999999999ap-2'
0.1 is 0x1.999999999999a times 2^-4. The "a" at the end means the digit 10 - in other words, 0.1 in binary floating point is very slightly larger than the "exact" value of 0.1 (because the final 0x0.99 is rounded up to 0x0.a). When you multiply this by 4, a power of two, the exponent shifts up (from 2^-4 to 2^-2) but the number is otherwise unchanged, so
4*0.1 == 0.4.
However, when you multiply by 3, the little tiny difference between 0x0.99 and 0x0.a0 (0x0.07) magnifies into a 0x0.15 error, which shows up as a one-digit error in the last position. This causes 0.1*3 to be very slightly larger than the rounded value of 0.3.
Python 3's float
repr is designed to be round-trippable, that is, the value shown should be exactly convertible into the original value. Therefore, it cannot display
0.1*3 exactly the same way, or the two different numbers would end up the same after round-tripping. Consequently, Python 3's
repr engine chooses to display one with a slight apparent error.