Weird Indexing problem in NumPy consuming too much memory

I am just learning indexing in NumPy and I am seeing these weird indexing issues with NumPy arrays. Here for instance, when I try to index this simple 1_D array;

import numpy as np


arr = np.ndarray([5, 4, 10, 20, 25, 81, 9, 7])
print(arr[2])

I get the following weird output

[[[[[[[0. 0. 0. ... 0. 0. 0.]
      [0. 0. 0. ... 0. 0. 0.]
      [0. 0. 0. ... 0. 0. 0.]
      ...
      [0. 0. 0. ... 0. 0. 0.]
      [0. 0. 0. ... 0. 0. 0.]
      [0. 0. 0. ... 0. 0. 0.]]

     [[0. 0. 0. ... 0. 0. 0.]
      [0. 0. 0. ... 0. 0. 0.]
      [0. 0. 0. ... 0. 0. 0.]

with tons and tons of zero matrices. And when I index a slightly bigger array, it gives me this problem;

numpy.core._exceptions._ArrayMemoryError: Unable to allocate 229. PiB for an array with shape (5, 4, 10, 20, 25, 81, 9, 7, 914, 23, 5, 7, 86) and data type float64

Why is NumPy behaving so weirdly and consuming so much memory just of a simple 1_D array and unable to perform indexing properly? For the IDE, I am using IntelliJ with a Conda venv.

You're using np.ndarray, when you probably meant to use np.array.

What you're doing here is creating an 8-dimensional array of 5 * 4 * 10 * 20 * 25 * 81 * 9 * 7 elements.