normalize multi dimensional numpy array using the last value along axis 1-CodePudding

I have the following numpy array :

A = np.array([[1,2,3,4,5],
             [15,25,35,45,55]])

I would like to create a new array with the same shape by dividing each dimension by the last element of the dimension

The output desired would be :

B = np.array([[0.2,0.4,0.6,0.8,1],
              [0.27272727,0.45454545,0.63636364,0.81818182,1]])

Any idea ?

CodePudding user response：

Slice the last element while keeping the dimensions and divide:

B = A/A[:,[-1]]  # slice with [] to keep the dimensions

or, better, to avoid an unnecessary copy:

B = A/A[:,-1,None]

output:

array([[0.2       , 0.4       , 0.6       , 0.8       , 1.        ],
       [0.27272727, 0.45454545, 0.63636364, 0.81818182, 1.        ]])

CodePudding user response：

You mean this?

B = np.array([[A[i][j]/A[i][len(A[i])-1] for j in range(0,len(A[i]))] for i in range(0,len(A))])

CodePudding user response：

You can achieve this using:

[list(map(lambda i: i / a[-1], a)) for a in A]

Result:

[[0.2, 0.4, 0.6, 0.8, 1.0], [0.2727272727272727, 0.45454545454545453, 0.6363636363636364, 0.8181818181818182, 1.0]]

CodePudding user response：

Adding on @mozway answer, it seems to be faster to take the last column and then add an axis with:

B = A/A[:,-1][:,None]

for instance.

See the benchmark: