Why does this piece of code return True when it clearly can be seen that the element [1, 1] is not present in the first array and what am I supposed to change in order to make it return False?
aux = np.asarray([[0, 1], [1, 2], [1, 3]])
np.asarray([1, 1]) in aux
True
Checking for equality for the two arrays broadcasts the 1d array so the == operator checks if the corresponding indices are equal.
>>> np.array([1, 1]) == aux
array([[False, True],
[ True, False],
[ True, False]])
Since none of the inner arrays are all True, no array in aux is completely equal to the other array. We can check for this using
np.any(np.all(np.array([1, 1]) == aux, axis=1))
timeit.timeit('np.any(np.all(a == b, axis=1))', setup='import numpy as np; a=np.random.rand(1000000, 2); b=np.asarray([-1,1])', number=10)/10)You can think of the in operator looping through an iterable and comparing each item for equality with what's being matched. I think what happens here can be demonstrated with the comparison to the first vector in the matrix/list:
>>> np.array([1, 1]) == np.array([0,1])
array([False, True])
and bool([False, True]) in Python == True so the in operator immediately returns.
==tests don't work reliably on floats. Giving anintexample will probably produce useless answers.inapplied to arrays should behave the same as with lists. Use lists if that's what you want. Otherwise, accept the fact thatnumpyarrays are multidimensional, and implement their own form ofin. Generally though, we don't useinwith arrays. There are better tools that give more control.