The floating point number 9007199254740993.0 is internally represented in memory as 9007199254740992.0 (due to how floating point works).
Python has special logic for comparing int with floats. Here it will try to compare the int 9007199254740993 with the float 9007199254740992.0. Python sees that the integer parts are different, so it will stop there and return False.
Comparing floats for equality is generally a bad idea anyways.
Floats should really only be used for approximate math. You need something like Java’s BigDecimal or BigInteger to handle floating point math with precision.
Comparing is fine, but it should be fuzzy. Less than and greater than are fine, so you basically should only be checking for withing a range of values, not a specific value.
I assume this is because that number is so large that it loses precision, in which case this is more of a quirk of floating point than a quirk of Python.
It’s both. As you said it’s because of loss of floating point precision, but it’s also with some of the quirks how Python compares int with float. These two together causes this strange behavior.
TL;DR:
In Python, following returns False.
9007199254740993 == 9007199254740993.0The floating point number 9007199254740993.0 is internally represented in memory as 9007199254740992.0 (due to how floating point works).
Python has special logic for comparing int with floats. Here it will try to compare the int 9007199254740993 with the float 9007199254740992.0. Python sees that the integer parts are different, so it will stop there and return False.
Comparing floats for equality is generally a bad idea anyways.
Floats should really only be used for approximate math. You need something like Java’s BigDecimal or BigInteger to handle floating point math with precision.
Looks like this is the equivalent for Python:
https://docs.python.org/3/library/decimal.html
Comparing is fine, but it should be fuzzy. Less than and greater than are fine, so you basically should only be checking for withing a range of values, not a specific value.
I assume this is because that number is so large that it loses precision, in which case this is more of a quirk of floating point than a quirk of Python.
Disclaimer: Have not read the article yet.
It’s both. As you said it’s because of loss of floating point precision, but it’s also with some of the quirks how Python compares int with float. These two together causes this strange behavior.