Numpy: broadcasting + boolean indexing

Question

I have the following numpy arrays

A: shape (n1, n2) array of float
B: shape (n2,) array of float
M: shape (n1, n2) array of bool

How do I turn the following pseduo-code inte efficient real code? The arrays may be huge, possibly > 100 million elements.

A[M] = ("B broadcast to shape (n1,n2)")[M]

seberg · Accepted Answer · 2012-09-03 11:18:15Z

2

Broadcasting is simple and memory efficient:

A, B, M = np.broadcast_arrays(A, B, M)

However using this B in your code A[M] = B[M] would not be memory efficient because B[M] has as many real elements as M has True values.

Instead use:

np.putmask(A, M, B)

Since B is repeated automatically with the putmask function, you should not even have to broadcast it. Though I guess it cannot hurt to do that.

answered Sep 3, 2012 at 11:18

seberg

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