Numpy - use values of array that meet condition

Question

I have two numpy arrays, one of a boolean type, the other float type. I want to get the rows of XY_coord where one of the colums has a row with True (so all rows in XY_coord with False-False in BOOL are removed. How do I do this?

BOOL = np.array([[False,  True],
           [False,  True],
           [False,  True],
           [False,  True],
           [ True,  True],
           [ True, False],
           [False, False],
           [False, False],
           [ True, False]])
       
XY_coord =    np.array([[-192.9594843 ,   78.17485294],
           [-182.2699483 ,   50.143909  ],
           [-171.5804122 ,   22.11296505],
           [ -51.11635646,  132.2664039 ],
           [ -40.42682039,  104.2354599 ],
           [ -29.73728432,   76.20451597],
           [  90.72677139,  186.3579548 ],
           [ 101.4163075 ,  158.3270108 ],
           [ 112.1058435 ,  130.2960669 ]])

I can do:

XY_coord[out[:,0]]
XY_coord[out[:,1]]

and append the outcome, but my matrix is much larger, so this will be inefficient.

Darina · Accepted Answer · 2020-10-15 14:30:22Z

1

There are many ways to solve this. Since you only care about dropping rows with False, False I would convert the BOOL array to a row index: keep_row = BOOL.sum(axis=1).astype('bool'). Then you can simply take XY_coord[keep_row] to get your filtered array.

answered Oct 15, 2020 at 14:30

Darina

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sai · Accepted Answer · 2020-10-15 14:30:44Z

1

Try this-

XY_coord[BOOL[:,0] | BOOL[:,1], :]

answered Oct 15, 2020 at 14:30

sai

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