Inserting zeros at multiple locations of an array in Python

Question

I have a list T2 and an array X . I want to insert zeroes at specific locations of X in accordance to T2 . For instance, for X[0] , the zeroes have to be inserted at all locations except the ones specified in T2[0] and for X[1] , the zeroes have to be inserted at all locations except the ones specified in T2[1] . I present the current and expected outputs.

import numpy as np

T2=[[0, 3, 5, 8, 9, 10, 11],[0, 2, 3, 5, 6, 8, 9, 10, 11]]

X=np.array([np.array([4.17551036e+02, 3.53856161e+02, 2.82754301e+02, 1.34119055e+02,
              6.34573886e+01, 2.08344718e+02, 1.00000000e-24])               ,
       np.array([4.17551036e+02, 3.32821605e+02, 2.94983702e+02, 2.78809292e+02,
              1.26991664e+02, 1.36026510e+02, 8.31512525e+01, 2.07329562e+02,
              1.00000000e-24])                                               ],
      dtype=object)

C1=0.0

index=0

for m in range(0,len(X)):
    for j in range(T2[m][-1]):
        if(j!=T2[m][index]):
            X[m] = np.insert(X[m], j, C1, axis=None)  
        else:
            index+=1

print([X])

The current output is

[array([array([4.17551036e+02, 0.00000000e+00, 0.00000000e+00, 3.53856161e+02,
              0.00000000e+00, 2.82754301e+02, 0.00000000e+00, 0.00000000e+00,
              1.34119055e+02, 6.34573886e+01, 2.08344718e+02, 1.00000000e-24]),
       array([0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
              0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
              0.00000000e+00, 4.17551036e+02, 3.32821605e+02, 2.94983702e+02,
              2.78809292e+02, 1.26991664e+02, 1.36026510e+02, 8.31512525e+01,
              2.07329562e+02, 1.00000000e-24])                               ],
      dtype=object)]

The expected output is

[array([array([4.17551036e+02, 0.00000000e+00, 0.00000000e+00, 3.53856161e+02,
              0.00000000e+00, 2.82754301e+02, 0.00000000e+00, 0.00000000e+00,
              1.34119055e+02, 6.34573886e+01, 2.08344718e+02, 1.00000000e-24]),
       array([4.17551036e+02, 0.00000000e+00, 3.32821605e+02, 2.94983702e+02,
              0.00000000e+00, 2.78809292e+02, 1.26991664e+02, 0.00000000e+00,
              1.36026510e+02, 8.31512525e+01, 2.07329562e+02, 1.00000000e-24]) ],
      dtype=object)]   

Answer 1

You are overcomplicating things. You can rephrase your problem as: Create an array with zeros everywhere except the indices in T2. Take those from X.

def make_array(indices, values):
    rtrn = np.zeros(np.max(indices) + 1, dtype=values.dtype)
    rtrn[indices] = values
    return rtrn


X = np.array([make_array(Ti, Xi) for Ti, Xi in zip(T2, X)], dtype=object)

Answer 2

You have two different tasks, the compound data structures complicate the problem, if you split the data in:

T1 = [0, 3, 5, 8, 9, 10, 11]

T2 = [0, 2, 3, 5, 6, 8, 9, 10, 11]

X1 = np.array([4.17551036e+02, 3.53856161e+02, 2.82754301e+02, 1.34119055e+02,
              6.34573886e+01, 2.08344718e+02, 1.00000000e-24])

X2 = np.array([4.17551036e+02, 3.32821605e+02, 2.94983702e+02, 2.78809292e+02,
              1.26991664e+02, 1.36026510e+02, 8.31512525e+01, 2.07329562e+02,
              1.00000000e-24])

Your two different problems can be solved with:

Y1 = np.zeros((12))
for i, value in zip(T1,X1):
    Y1[i] = value

Y2 = np.zeros((12))
for i1, i2 in enumerate(T2):
    Y2[i2] = X2[i1]