negative zero in python

It makes a difference in the atan2() function (at least, in some implementations). In my Python 3.1 and 3.2 on Windows (which is based on the underlying C implementation, according to the note CPython implementation detail near the bottom of the Python math module documentation):

>>> import math
>>> math.atan2(0.0, 0.0)
0.0
>>> math.atan2(-0.0, 0.0)
-0.0
>>> math.atan2(0.0, -0.0)
3.141592653589793
>>> math.atan2(-0.0, -0.0)
-3.141592653589793

math.copysign() treats -0.0 and +0.0 differently, unless you are running Python on a weird platform:

math.copysign(x, y)
     Return x with the sign of y. On a platform that supports signed zeros, copysign(1.0, -0.0) returns -1.0.

>>> import math
>>> math.copysign(1, -0.0)
-1.0
>>> math.copysign(1, 0.0)
1.0

Yes, there is a difference between 0.0 and -0.0 (though Python won't let me reproduce it :-P). If you divide a positive number by 0.0, you get positive infinity; if you divide that same number by -0.0 you get negative infinity.

Beyond that, though, there is no practical difference between the two values.

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
>>> 

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[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.