In Python, it is possible to check if a float contains an integer value using n.is_integer(), based on this QA: How to check if a float value is a whole number.
Does numpy have a similar operation that can be applied to arrays? Something that would allow the following:
>>> x = np.array([1.0 2.1 3.0 3.9])
>>> mask = np.is_integer(x)
>>> mask
array([True, False, True, False], dtype=bool)
It is possible to do something like
>>> mask = (x == np.floor(x))
or
>>> mask = (x == np.round(x))
but they involve calling extra methods and creating a bunch of temp arrays that could be potentially avoided.
Does numpy have a vectorized function that checks for fractional parts of floats in a way similar to Python's float.is_integer?
From what I can tell, there is no such function that returns a boolean array indicating whether floats have a fractional part or not. The closest I can find is np.modf which returns the fractional and integer parts, but that creates two float arrays (at least temporarily), so it might not be best memory-wise.
If you're happy working in place, you can try something like:
>>> np.mod(x, 1, out=x)
>>> mask = (x == 0)
This should save memory versus using round or floor (where you have to keep x around), but of course you lose the original x.
The other option is to ask for it to be implemented in Numpy, or implement it yourself.