Understanding the working mechanis of python unary arithmetic operators.
hongy...@gmail.com
hongyi.zhao at gmail.com
Sun Oct 3 02:18:05 EDT 2021
On Saturday, October 2, 2021 at 4:59:54 PM UTC+8, ju... at diegidio.name wrote:
> On Saturday, 2 October 2021 at 10:34:27 UTC+2, hongy... at gmail.com wrote:
> > See the following testings:
> >
> > In [24]: a=3.1415926535897932384626433832795028841971
> > In [27]: -a
> > Out[27]: -3.141592653589793
> You've never heard of floating-point? Double precision has 53 significant bits of mantissa, corresponding approximately to 16 decimal digits.
> <https://en.wikipedia.org/wiki/Double-precision_floating-point_format#IEEE_754_double-precision_binary_floating-point_format:_binary64>
> > In [17]: ~-+1
> > Out[17]: 0
> << The unary ~ (invert) operator yields the bitwise inversion of its integer argument. The bitwise inversion of x is defined as -(x+1). It only applies to integral numbers or to custom objects that override the __invert__() special method. >>
> <https://docs.python.org/3/reference/expressions.html#unary-arithmetic-and-bitwise-operations>
A further inference based on the above description:
Let us consider this equation: -(x+1) = x, the solution is -0.5, which is not an integer. So we can safely come to a conclusion:
If bool(a) == True, \forall a \in integer, then ~bool(a) == False; and vice versa.
This is exactly the theoretical basis to filter some specific columns in pandas, just as the issue discussed here [1].
[1] https://github.com/pandas-dev/pandas/issues/43832#issue-1013375587
HZ
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