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I encountered negative zero in output from python; it's created for example as follows:
k = 0.0 print(-k) The output will be -0.0.
However, when I compare the -k to 0.0 for equality, it yields True. Is there any difference between 0.0 and -0.0 (I don't care that they presumably have different internal representation; I only care about their behavior in a program.) Is there any hidden traps I should be aware of?
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But python can't represent integer negative zero.
One may obtain negative zero as the result of certain computations, for instance as the result of arithmetic underflow on a negative number (other results may also be possible), or −1.0×0.0 , or simply as −0.0 .
n = float(input("Input a number: ")) if n >= 0: if n == 0: print("It is Zero! ") else: print("Number is Positive number. ") else: print("Number is Negative number. ")
A unary mathematical expression consists of only one component or element, and in Python the plus and minus signs can be used as a single element paired with a value to return the value's identity ( + ), or change the sign of the value ( - ). With a negative value the plus sign returns the same negative value.
Check out −0 (number) in Wikipedia
Basically IEEE does actually define a negative zero.
And by this definition for all purposes:
-0.0 == +0.0 == 0 I agree with aaronasterling that -0.0 and +0.0 are different objects. Making them equal (equality operator) makes sure that subtle bugs are not introduced in the code.
Think of a * b == c * d
>>> a = 3.4 >>> b =4.4 >>> c = -0.0 >>> d = +0.0 >>> a*c -0.0 >>> b*d 0.0 >>> a*c == b*d True >>> [Edit: More info based on comments]
When I said for all practical purposes, I had chosen the word rather hastily. I meant standard equality comparison.
As the reference says, the IEEE standard defines comparison so that +0 = -0, rather than -0 < +0. Although it would be possible always to ignore the sign of zero, the IEEE standard does not do so. When a multiplication or division involves a signed zero, the usual sign rules apply in computing the sign of the answer.
Operations like divmod and atan2 exhibit this behavior. In fact, atan2 complies with the IEEE definition as does the underlying "C" lib.
>>> divmod(-0.0,100) (-0.0, 0.0) >>> divmod(+0.0,100) (0.0, 0.0) >>> math.atan2(0.0, 0.0) == math.atan2(-0.0, 0.0) True >>> math.atan2(0.0, -0.0) == math.atan2(-0.0, -0.0) False One way is to find out through the documentation, if the implementation complies with IEEE behavior . It also seems from the discussion that there are subtle platform variations too.
However this aspect (IEEE definition compliance) has not been respected everywhere. See the rejection of PEP 754 due to disinterest! I am not sure if this was picked up later.
See also What Every Computer Scientist Should Know About Floating-Point Arithmetic.
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