Numpy 如何将二维数组的元素相加？

Question

Say I have a numpy 2D array:

>>> t
array([[-0.00880717,  0.02522217, -0.01014062],
       [-0.00866732,  0.01737254,  0.05396272]])

Now using array slicing, I can quickly obtain all items in all rows starting from the column with index 1 and sum them up:

>>> t[:, 1:].sum()
0.08641680780899146

To verify manually, here is what happens:

>>> 0.02522217+0.01737254+-0.01014062+0.05396272
0.08641680999999998

Just to understand the numpy array operations better, is numpy first going over all rows and summing the items of the rows, or is it going down one column, and then down the next one?

Answer 1

Thanks for asking your question, @TMOTTM!

The way NumPy's sum semantics work are documented in theNumPy manual .

To summarize the manual while injecting my own understanding:

1. arr.sum() called without an axis argument simply sums up everything in the array. It is the most straightforward semantic operation to implement.
2. arr.sum(axis=0) will collapse axis 0 (the first axis) while summing.
3. arr.sum(axis=k) will collapse axis k while performing a summation.

Canonically, axis 0 is semantically recognized as the row-wise axis, axis 1 is the column-wise axis, and axis 2 is the depth-wise axis, and anything higher goes into hypercubes which are not easily described in words.

Made concrete:

1. In a 2D array, to collapse the rows (ie sum column-wise), do arr.sum(axis=0) .
2. In a 2D array, to collapse the columns (ie sum row-wise), do arr.sum(axis=1) .

At the end of the day, point 3 is the one you want to remember: reason carefully about which axis you wish to collapse, and you'll never go wrong!