Python Numpy - numpy axis performance
You don't compute the same thing.
The first two commands only compute one row/column out of the entire array.
a[0, :].sum().shape # sums just the first row only()
The second two commands, sum the entire contents of the 2D array, but along a certain axis. That way, you don't get a single result (as in the first two commands), but an 1D array of sums.
a.sum(axis=0).shape # computes the row-wise sum for each column(5000,)
In summary, the two sets of commands do different things.
aarray([[1, 6, 9, 1, 6], [5, 6, 9, 1, 3], [5, 0, 3, 5, 7], [2, 8, 3, 8, 6], [3, 4, 8, 5, 0]])a[0, :]array([1, 6, 9, 1, 6])a[0, :].sum()23
a.sum(axis=0)array([16, 24, 32, 20, 22])