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pythonarraysperformancenumpymultiplication
Neil G
Neil G

Reputation: 33202

How do I multiply corresponding elements in numpy arrays efficiently?

Given a numpy array x of shape (m,) and a numpy array y of shape (m/n,), how do I multiply x by corresponding elements of y efficiently?

Here's my best attempt:

In [13]: x = np.array([1, 5, 3, 2, 9, 1])

In [14]: y = np.array([2, 4, 6])

In [15]: n = 2

In [16]: (y[:, np.newaxis] * x.reshape((-1, n))).flatten()
Out[16]: array([ 2, 10, 12,  8, 54,  6])

Upvotes: 2

Views: 594

Answers (1)

Alex Riley
Alex Riley

Reputation: 176810

Your solution looks pretty good to me.

If you wanted to speed it up slightly, you could:

  • Use ravel() instead of flatten() (the former will return a view if possible, the latter always returns a copy).

  • Reshape x in Fortran order to avoid the overhead of another indexing operation on y (although subsequent timings suggest this speedup is negligible)

So rewritten the multiplication becomes:

>>> (x.reshape((2, -1), order='f') * y).ravel('f')
array([ 2, 10, 12,  8, 54,  6])

Timings:

>>> %timeit (y[:, np.newaxis] * x.reshape((-1, n))).flatten()
100000 loops, best of 3: 7.4 µs per loop

>>> %timeit (x.reshape((n, -1), order='f') * y).ravel('f')
100000 loops, best of 3: 4.98 µs per loop

Upvotes: 3

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