On Saturday, October 2, 2021 at 4:59:54 PM UTC+8, ju...@diegidio.name wrote: > On Saturday, 2 October 2021 at 10:34:27 UTC+2, hongy...@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 -- https://mail.python.org/mailman/listinfo/python-list
