On Fri, 2019-01-11 at 12:32 -0800, Keith Goodman wrote:

> I remember back when a.sum(axis=0) was much slower than a.sum(axis=1)

> for something like a=np.ones((1000, 1000)). But now it runs in about

> the same time. How does numpy do it?

>

"now" is since numpy 1.7 or so :).

> Does numpy do something like

>

> for i in range(a.shape[0]):

> for j in range(x.shape[1]):

> result[j] += a[i, j]

Yeah, numpy reorders the operation. Maybe a bit closer to what happens

is to write it down with the result as a 2D array (as happens with

keepdims), since internally that is how numpy operates on it (although

it may optimize the `i*0` away):

for i in range(a.shape[0]):

for j in range(a.shape[1]):

# If sum is along axis 0:

result[i*0, j] += a[i, j]

Since it doesn't matter which of the loop is the innermost one, the

machinery is capable of reordering them. I think it learned it with 1.7

(because that added a lot), but maybe it was even earlier.

- Sebastian

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