For example: I have a = np.array([123, 412, 444])
and b = np.array([123, 321])
I want to know if a
contains all the elements in b
. Is there a simple operation for this? In this case that would not be true.
You could alway use a set:
>>> a = numpy.array([123, 412, 444])
>>> b = numpy.array([123, 321])
>>> set(b) in set(a)
False
Or with newer versions of numpy:
>>> numpy.in1d(b,a)
array([ True, False], dtype=bool)
If you want just 'the answer' rather than an array:
>>> numpy.in1d(b,a).all()
False
Or (least desirable):
>>> numpy.array([x in a for x in b])
array([ True, False], dtype=bool)
Looping is slowish on numpy arrays and should be avoided.
You can use set difference to determine what you are looking for. Numpy has a built-in function called numpy.setdiff1d(ar1, ar2):
Return the sorted, unique values in ar1 that are not in ar2.
Example for your case:
>>> a = np.array([123, 412, 444])
>>> b = np.array([123, 321])
>>> diff = np.setdiff1d(b, a)
>>> print diff
array([321])
>>> if diff.size:
>>> print "Not passed"
So for your case, you would do a set difference you would subtract a from b and obtain an array with elements in b which are not in a. Then you can check if that was empty or not. As you can see, the output is 312
, which is an entry present in a
but not in b
; the length of it is now larger then zero, therefore there were elements in b
which were not present in a
.
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