When comparing floats to integers, some pairs of values take much longer to be evaluated than other values of a similar magnitude.
For example:
>>> import timeit
>>> timeit.timeit("562949953420000.7 < 562949953421000") # run 1 million times
0.5387085462592742
But if the float or integer is made smaller or larger by a certain amount, the comparison runs much more quickly:
>>> timeit.timeit("562949953420000.7 < 562949953422000") # integer increased by 1000
0.1481498428446173
>>> timeit.timeit("562949953423001.8 < 562949953421000") # float increased by 3001.1
0.1459577925548956
Changing the comparison operator (e.g. using == or > instead) does not affect the times in any noticeable way.
This is not solely related to magnitude because picking larger or smaller values can result in faster comparisons, so I suspect it is down to some unfortunate way the bits line up.
Clearly, comparing these values is more than fast enough for most use cases. I am simply curious as to why Python seems to struggle more with some pairs of values than with others.
