Be aware that NumPy is built to support linear algebra. A 1D NumPy array may correspond to a linear algebra vector; a 2D array to a matrix; and 3D, 4D, or all ndarray to tensors. So, when appropriate, NumPy supports linear algebra operations, such as matrix products, transposition, matrix inversion, and so on, for arrays. Most NumPy linear algebra functionality is supported in the linalg module. The following is a list of commonly used NumPy linear algebra functions:
Some of these are ndarray methods, others are in the linalg module you need to import. So we've actually been demonstrating transpose up to this point in earlier examples. Notice that we were using transpose here to swap around rows and columns.
This is transposition in arr4:
I said arr4 was arr3 and we switched around the axes. So axis 0 would still be axis 0, but axis 1 would be axis 2 of the old array, and axis 2 would be axis 1 of the old array.
Now let's see some other examples. Let's see a demonstration of reshape...