Conditional operations on numpy arrays

Question

I\'m new to NumPy, and I\'ve encountered a problem with running some conditional statements on numpy arrays. Let\'s say I have 3 numpy arrays that look like this:

a:

Answer 1

You can use numpy.where:

np.where((a > 3) & (b > 8), c + b*2, c)
#array([[ 0, 18,  0,  0],
#       [ 0,  0,  0,  0],
#       [ 0,  0,  0,  0]])

Or arithmetically:

c + b*2 * ((a > 3) & (b > 8))
#array([[ 0, 18,  0,  0],
#       [ 0,  0,  0,  0],
#       [ 0,  0,  0,  0]])

Answer 2

The problem is that you mask the receiving part, but do not mask the sender part. As a result:

c[(a > 3) & (b > 8)]+=b*2
# ^ 1x1 matrix        ^3x4 matrix

The dimensions are not the same. Given you want to perform element-wise addition (based on your example), you can simply add the slicing to the right part as well:

c[(a > 3) & (b > 8)]+=b[(a > 3) & (b > 8)]*2

or make it more efficient:

mask = (a > 3) & (b > 8)
c[mask] += b[mask]*2

Answer 3

A slight change in the numpy expression would get the desired results:

c += ((a > 3) & (b > 8)) * b*2

Here First I create a mask matrix with boolean values, from ((a > 3) & (b > 8)), then multiply the matrix with b*2 which in turn generates a 3x4 matrix which can be easily added to c