Change values of a numpy array based on certain condition
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 ?
Answers:
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.
I found another answer:
A = np.where(A<0, A+5, A)
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 ?
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.
I found another answer:
A = np.where(A<0, A+5, A)