Clarification in the fp appendix suggested on c.l.py by Michael Chermside.
Also replaced a *star* style emphasis in the Representation Error section with an \emph{} thingie.
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@ -4180,7 +4180,8 @@ turns out that's enough (on most machines) so that
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Note that this is in the very nature of binary floating-point: this is
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not a bug in Python, it is not a bug in your code either, and you'll
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see the same kind of thing in all languages that support your
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hardware's floating-point arithmetic.
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hardware's floating-point arithmetic (although some languages may
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not \emph{display} the difference by default, or in all output modes).
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Python's builtin \function{str()} function produces only 12
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significant digits, and you may wish to use that instead. It's
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@ -4326,7 +4327,7 @@ precision is that over 2**56, or
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Note that since we rounded up, this is actually a little bit larger than
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1/10; if we had not rounded up, the quotient would have been a little
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bit smaller than 1/10. But in no case can it be *exactly* 1/10!
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bit smaller than 1/10. But in no case can it be \emph{exactly} 1/10!
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So the computer never ``sees'' 1/10: what it sees is the exact
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fraction given above, the best 754 double approximation it can get:
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