numpy, do multiple operations cause intermediate arrays to be created?

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I'm wondering if these two are equivalent:

import numpy as np

a = np.arange(100000) + 1

# 1
b = 10 * np.log10(a)

# 2
c = np.empty_like(a)
c = np.multiply(10, np.log10(a, out=c), out=c)

More precisely, I wonder if numpy found some way to create array b without having an intermediate array that needs to be allocated and thrown away later for the result of the log operation. Of course, this only matters for very big arrays.

numpy python performance
1 Answers

In terms of computing time, they seem to be roughly similar, although the first version is a bit better:

In [1]: import numpy as np

In [2]: a = np.arange(100_000, dtype=float) + 1

In [3]: def f(a):
   ...:     b = 10 * np.log10(a)
   ...:

In [4]: def g(a):
   ...:     c = np.empty_like(a)
   ...:     c = np.multiply(10, np.log10(a, out=c), out=c)
   ...:

In [5]: %timeit f(a)
759 µs ± 52.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

In [6]: %timeit g(a)
877 µs ± 39.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
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