simple way to select numpy subarray using boolean conditional vector in python 3

Question

How can you select only the columns of a 2-d numpy array that correspond to a conditional boolean vector?

Say you have a 10x10 matrix, generated by, say:

a = np.random.randint(0,1,(10,10))
a = 
array([[4, 9, 1, 9, 5, 2, 1, 7, 6, 5],
       [5, 4, 2, 4, 8, 1, 5, 5, 7, 5],
       [3, 8, 7, 4, 3, 4, 8, 8, 8, 3],
       [5, 4, 4, 4, 9, 6, 7, 1, 6, 8],
       [8, 3, 2, 1, 7, 5, 8, 8, 4, 9],
       [9, 5, 6, 8, 6, 8, 1, 4, 4, 5],
       [5, 4, 3, 2, 8, 3, 2, 2, 8, 6],
       [2, 5, 4, 5, 9, 7, 9, 2, 5, 6],
       [4, 5, 9, 7, 3, 1, 5, 7, 4, 8],
       [6, 1, 3, 8, 8, 3, 2, 6, 6, 7]])

and you want to cut out all the rows corresponding to a vector containing (True/False or 0/1), like, say:

b = np.random.randint(0,2,10)
b = 
array([0, 1, 1, 0, 1, 0, 1, 1, 1, 1])

cancerconnector · Accepted Answer · 2016-02-01 19:06:27Z

2

I spent some time trying to find the simple syntax to return only specified colummns in a numpy array in python 3 and finally have it figured out. There are a number of other threads which show more complicated ways to do this, so I thought I would put the simple solution here. This will be very obvious to more experienced python users, but for a beginner like me, it would have been useful.

The simplest way is:

new_matrix = a[:,b==1]

which yields:

new_matrix = 

array([[9, 1, 5, 1, 7, 6, 5],
       [4, 2, 8, 5, 5, 7, 5],
       [8, 7, 3, 8, 8, 8, 3],
       [4, 4, 9, 7, 1, 6, 8],
       [3, 2, 7, 8, 8, 4, 9],
       [5, 6, 6, 1, 4, 4, 5],
       [4, 3, 8, 2, 2, 8, 6],
       [5, 4, 9, 9, 2, 5, 6],
       [5, 9, 3, 5, 7, 4, 8],
       [1, 3, 8, 2, 6, 6, 7]])

This would have saved me a lot of time.

edited Feb 1, 2016 at 19:06

answered Feb 1, 2016 at 17:08

cancerconnector

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1 Comment

hpaulj Over a year ago

That expression selects columns.

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