Question

I need to find if a numpy array is inside other numpy array, but it seems to work different to python lists.
I tried to search this question in numpy documentation and internet, but not answer.
This is an example:


import numpy as np

m1=np.array([[1,2,3],[5,3,4]])
m2=np.array([5,4,3])
m2 in m1
True
m3=[[1,2,3],[5,3,4]]
m4=[5,4,3]
m4 in m3
False

In numpy I obtain True but with Python lists I obtain False. Is there any numpy function to make this work?

Thanks.

Answer 1

To get the same behavior as in for lists, you could do something like this:

any(np.all(row == m2) for row in m1)

That does the loop over rows in python, which isn’t ideal, but it should work.

To understand what’s going on with the numpy in, here’s a description of the semantics of in from Robert Kern on the numpy mailing list:

It dates back to Numeric’s semantics for bool(some_array), which would
be True if any of the elements were nonzero. Just like any other
iterable container in Python, x in y will essentially do

for row in y:
   if x == row:
       return True
return False

Iterate along the first axis of y and compare by boolean equality. In
Numeric/numpy’s case, this comparison is broadcasted. So that’s why
[3,6,4] works, because there is one row where 3 is in the first
column. [4,2,345] doesn’t work because the 4 and the 2 are not in
those columns.

Probably, this should be considered a mistake during the transition to
numpy’s semantics of having bool(some_array) raise an exception.
scalar in array should probably work as-is for an ND array, but
there are several different possible semantics for array in array
that should be explicitly spelled out, much like bool(some_array).