How can I implement matlabs ``ismember()`` command in Python?

Question

here is my problem: I would like to create a boolean matrix B that contains True everywhere that matrix A has a value contained in vector v. One inconvenient so

Answer 1

I think the closest you'll get is numpy.ismember1d, but it won't work well with your example. I think your solution (B = (A==v[0]) + (A==v[1])) may actually be the best one.

Answer 2

Since Numpy version 1.4 there's a new function, in1d() that's the equivalent of ismember() in matlab: http://docs.scipy.org/doc/numpy-1.6.0/reference/generated/numpy.in1d.html. But as ars points out, it only returns a 1d array.

Answer 3

I don't know much numpy, be here's a raw python one:

>>> A = [[0,1,2], [1,2,3], [2,3,4]]
>>> v = [1,2]
>>> B = [map(lambda val: val in v, a) for a in A]
>>>
>>> B
[[False, True, True], [True, True, False], [True, False, False]]

Edit: As Brooks Moses notes and some simple timing seems to show, this one is probably be better:

>>> B = [ [val in v for val in a] for a in A]

Answer 4

Here's a naive one-liner:

[any (value in item for value in v) for item in A]

Sample output:

>>> A = ( [0,1,2], [1,2,3], [2,3,4] )
>>> v = [1,2]
>>> [any (value in item for value in v) for item in A]
[True, True, True]
>>> v = [1]
>>> [any (value in item for value in v) for item in A]
[True, True, False]

It's a very Pythonic approach, but I'm certain it won't scale well on large arrays or vectors because Python's in operator is a linear search (on lists/tuples, at least).

As Brooks Moses pointed out in the below comment, the output should be a 3x3 matrix. That's why you give sample output in your questions. (Thanks Brooks)

>>> v=[1,2]
>>> [ [item in v for item in row] for row in A]
[[False, True, True], [True, True, False], [True, False, False]]
>>> v=[1]
>>> [ [item in v for item in row] for row in A]
[[False, True, False], [True, False, False], [False, False, False]]

Answer 5

Using numpy primitives:

>>> import numpy as np
>>> A = np.array([[0,1,2], [1,2,3], [2,3,4]])
>>> v = [1,2]
>>> print np.vectorize(lambda x: x in v)(A)
[[False  True  True]
 [ True  True False]
 [ True False False]]

For non-tiny inputs convert v to a set first for a large speedup.

To use numpy.setmember1d:

Auniq, Ainv = np.unique1d(A, return_inverse=True)
result = np.take(np.setmember1d(Auniq, np.unique1d(v)), Ainv).reshape(A.shape)

Answer 6

Alas, setmember1d as it exists in numpy is broken when either array has duplicated elements (as A does here). Download this version, call it e.g sem.py somewhere on your sys.path, add to it a first line import numpy as nm, and THEN this finally works:

>>> import sem
>>> print sem.setmember1d(A.reshape(A.size), v).reshape(A.shape)
[[False True True]
 [True True False]
 [True False False]]

Note the difference wrt @Aants' similar answer: this version has the second row of the resulting bool array correct, while his version (using the setmember1d that comes as part of numpy) incorrectly has the second row as all Trues.