How to Reshape Multidimensional Arrays in NumPy: A Step-by-Step Guide

Intuition and Implementation of Multidimensional Array Reshaping in NumPy

In NumPy, reshaping multidimensional arrays is essential for data manipulation and transformation. Here's an intuitive approach, with a detailed example:

Understanding the Reshaping Process

Reshaping arrays involves two sequential steps:

• Permutation of Axes: Adjust the order of dimensions to align with the desired output.
• Reshaping Operation: Modify the shape of the array to match the intended dimensions.

Example: Reshaping a 4D Array to a 2D Array

Consider the given 4D array:

array([[[[ 0,  0],
         [ 0,  0]],

        [[ 5, 10],
         [15, 20]]],


       [[[ 6, 12],
         [18, 24]],

        [[ 7, 14],
         [21, 28]]]])

To reshape it to (4,4), follow the back-tracking method:

1. Permutation of Axes: To match the output strides, permute the axes to (2, 0, 3, 1).

   reshaped_array = a.transpose((2, 0, 3, 1))

2. Reshaping Operation: Reshape the permuted array to the desired shape.

   reshaped_array = reshaped_array.reshape(4,4)

Output:

array([[ 0,  5,  0, 10],
       [ 6,  7, 12, 14],
       [ 0, 15,  0, 20],
       [18, 21, 24, 28]])

Additional Examples

For further understanding, refer to these additional examples that demonstrate the reshaping of various multidimensional arrays:

• [Python Reshape 3d array into 2d](https://stackoverflow.com/questions/21777810/python-reshape-3d-array-into-2d)
• [Numpy change shape from (3, 512, 660, 4) to (3,2048,660,1)](https://stackoverflow.com/questions/48036140/numpy-change-shape-from-3-512-660-4-to-3-2048-660-1)

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