Array Reshaping NumPy

 Reshaping arrays is a fundamental operation in NumPy. It allows you to change the way elements are arranged within the array without modifying the actual data. Here's a breakdown of the most common methods:

1. Using reshape:

• This is the most versatile method, taking the original array and a new shape as arguments.
• The new shape must be compatible with the total number of elements in the original array.
• You can use -1 in the new shape to automatically calculate the missing dimension size.

2. Using ravel:

• Converts the array into a one-dimensional (flat) array.
• Useful for operations that require 1D data.

3. Using transpose:

• Swaps the axes of the array.
• Primarily used for 2D arrays to switch between row-major and column-major order.

 

 

 

Important points to remember:

• Reshaping doesn't change the data, just its interpretation.
• The total number of elements must remain constant.
• Be cautious with -1 in reshape to avoid unexpected results.
• Choose the method that best suits your desired outcome and array dimensions.