I have a list of tuples like this:

listOfTuples = [(0, 1), (0, 2), (3, 1)]

and an array that could look like this:

myArray = np.array([-2, 9, 5])

Furthermore, I have an array with Boolean expressions which I created like this:

dummyArray = np.array([0, 1, 0.6])
myBooleanArray =  dummyArray < 1

myBooleanArray therefore looks like this:

array([True, False, True], dtype=bool)

Now I would like to extract values from listOfTuples and myArray based on myBooleanArray. For myArray it is straight forward and I can just use:

myArray[myBooleanArray]

which gives me the desired output

[-2  5]

However, when I use

listOfTuples[myBooleanArray]

I receive

TypeError: only integer arrays with one element can be converted to anindex

A workaround would be to convert this list to an array first by doing:

np.array(listOfTuples)[myBooleanArray]

which yields

[[0 1][3 1]]

Is there any smarter way of doing this? My desired output would be

[(0, 1), (3, 1)]

Python list object, unlike Numpy array, doesn't support boolean indexing directly. For that you could use itertools.compress function:

>>> from itertools import compress
>>> list(compress(listOfTuples,bool_array))
[(0, 1), (3, 1)]

Note that one of the advantages of compress along side its functional structure which can be very useful in many cases, is that it returns a generator and its very memory efficient in cases where you have a very large list object to filter.

If you want you can also to loop over the result if you wish to process the items one by one instead of converting the whole object to a list:

for item in compress(listOfTuples,bool_array):#do stuff