So Python has positive and negative infinity:
This just seems like the type of feature that has to have some caveat. Is there anything I should be aware of?
So does C99.
The IEEE 754 floating point representation used by all modern processors has several special bit patterns reserved for positive infinity (sign=0, exp=~0, frac=0), negative infinity (sign=1, exp=~0, frac=0), and many NaN (Not a Number: exp=~0, frac≠0).
All you need to worry about: some arithmetic may cause floating point exceptions/traps, but those aren’t limited to only these “interesting” constants.
You can still get not-a-number (NaN) values from simple arithmetic involving
>>> 0 * float("inf") nan
Note that you will normally not get an
inf value through usual arithmetic calculations:
>>> 2.0**2 4.0 >>> _**2 16.0 >>> _**2 256.0 >>> _**2 65536.0 >>> _**2 4294967296.0 >>> _**2 1.8446744073709552e+19 >>> _**2 3.4028236692093846e+38 >>> _**2 1.157920892373162e+77 >>> _**2 1.3407807929942597e+154 >>> _**2 Traceback (most recent call last): File "<stdin>", line 1, in ? OverflowError: (34, 'Numerical result out of range')
inf value is considered a very special value with unusual semantics, so it’s better to know about an
OverflowError straight away through an exception, rather than having an
inf value silently injected into your calculations.
Python’s implementation follows the IEEE-754 standard pretty well, which you can use as a guidance, but it relies on the underlying system it was compiled on, so platform differences may occur. Recently¹, a fix has been applied that allows "infinity" as well as "inf", but that’s of minor importance here.
The following sections equally well apply to any language that implements IEEE floating point arithmetic correctly, it is not specific to just Python.
When dealing with infinity and greater-than
> or less-than
< operators, the following counts:
+infis higher than
-infis lower than
+infis neither higher nor lower than
-infis neither higher nor lower than
NaNis false (
infis neither higher, nor lower than
When compared for equality,
+inf are equal, as are
-inf. This is a much debated issue and may sound controversial to you, but it’s in the IEEE standard and Python behaves just like that.
+inf is unequal to
-inf and everything, including
NaN itself, is unequal to
Most calculations with infinity will yield infinity, unless both operands are infinity, when the operation division or modulo, or with multiplication with zero, there are some special rules to keep in mind:
when multiplied by zero, for which the result is undefined, it yields
when dividing any number (except infinity itself) by infinity, which yields
when dividing (including modulo) positive or negative infinity by positive or negative infinity, the result is undefined, so
when subtracting, the results may be surprising, but follow common math sense:
inf - inf, the result is undefined:
inf - -inf, the result is
-inf - inf, the result is
-inf - -inf, the result is undefined:
when adding, it can be similarly surprising too:
inf + inf, the result is
inf + -inf, the result is undefined:
-inf + inf, the result is undefined:
-inf + -inf, the result is
** is tricky, as it doesn’t behave as it should. It throws an overflow exception when the result with two real numbers is too high to fit a double precision float (it should return infinity), but when the input is
-inf, it behaves correctly and returns either
0.0. When the second argument is
NaN, it returns
NaN, unless the first argument is
1.0. There are more issues, not all covered in the docs.
math.exp suffers the same issues as
math.pow. A solution to fix this for overflow is to use code similar to this:
try: res = math.exp(420000) except OverflowError: res = float('inf')
Note 1: as an additional caveat, that as defined by the IEEE standard, if your calculation result under-or overflows, the result will not be an under- or overflow error, but positive or negative infinity:
1e308 * 10.0 yields
Note 2: because any calculation with
NaN and any comparison to
NaN itself is
false, you should use the
math.isnan function to determine if a number is indeed
Note 3: though Python supports writing
float('-NaN'), the sign is ignored, because there exists no sign on
NaN internally. If you divide
-inf / +inf, the result is
-NaN (there is no such thing).
Note 4: be careful to rely on any of the above, as Python relies on the C or Java library it was compiled for and not all underlying systems implement all this behavior correctly. If you want to be sure, test for infinity prior to doing your calculations.
¹) Recently means since version 3.2.
²) Floating points support positive and negative zero, so:
x / float('inf') keeps its sign and
-1 / float('inf') yields
1 / float(-inf) yields
1 / float('inf') yields
-1/ float(-inf) yields
0.0. In addition,
0.0 == -0.0 is
true, you have to manually check the sign if you don’t want it to be true.
I found a caveat that no one so far has mentioned. I don’t know if it will come up often in practical situations, but here it is for the sake of completeness.
Usually, calculating a number modulo infinity returns itself as a float, but a fraction modulo infinity returns
nan (not a number). Here is an example:
>>> from fractions import Fraction >>> from math import inf >>> 3 % inf 3.0 >>> 3.5 % inf 3.5 >>> Fraction('1/3') % inf nan
I filed an issue on the Python bug tracker. It can be seen at https://bugs.python.org/issue32968.
Update: this will be fixed in Python 3.8.
A VERY BAD CAVEAT : Division by Zero
1/x fraction, up to
x = 1e-323 it is
inf but when
x = 1e-324 or little it throws
>>> 1/1e-323 inf >>> 1/1e-324 Traceback (most recent call last): File "<stdin>", line 1, in <module> ZeroDivisionError: float division by zero
so be cautious!