根据特定条件更改一维numpy数组的值

Question

Very basic question:

Suppose I have a 1D numpy array (A) containing 5 elements:

A = np.array([ -4.0, 5.0, -3.5, 5.4, -5.9])

I need to add, 5 to all the elements of A that are lesser than zero. What is the numpy way to do this without for-looping ?

Answer 1

It can be done using mask:

A[A < 0] += 5

The way it works is - the expression A < 0 returns a boolean array. Each cell corresponds to the predicate applied on the matching cell. In the current example:

A < 0  # [ True False  True False  True]  

And then, the action is applied only on the cells that match the predicate. So in this example, it works only on the True cells.

Answer 2

我找到了另一个答案：

A = np.where(A < 0, A + 5, A)