numpy for Numerical and Scientific Computing

As you approach the end of this introduction to constructing arrays in numpy, read this section to familiarize yourself with various numpy methods used throughout the course. Specifically, the need for methods such as shape, size, linspace, reshape, eye, and zeros often arise when manipulating arrays.

Beware of copies

One has to be a bit careful with copying objects in Python. By default, if you just assign one object to a new name, it does a shallow copy, which means that both names point to the same memory. So if you change something in the original, it also changes in the new copy. 
A[0,:]
array([3, 4, 2, 1, 3])
A1 = A
 A1[0,0] = 4
 A[0,0]
4

To actually create a copy that is not linked back to the original, you have to make a deep copy, which creates a new space in memory and a new pointer and copies the original object to the new memory location

A1 = A.copy()
 A1[0,0] = 6
 A[0,0]
4

You can also replace sub-matrices of a matrix with new data, provided that the dimensions are compatible. (Make sure that the sub-matrix we are replacing below truly has 2 rows and 2 columns, which is what np.eye(2) will produce)

A[:2,:2] = np.eye(2)
 A
array([[1, 0, 2, 1, 3],
       [0, 1, 1, 1, 1],
       [4, 0, 2, 0, 4]])


Reducing matrix dimensions

Sometimes the output of some operation ends up being a matrix of one column or one row. We can reduce it to become a vector. There are two functions that can do that, flatten and ravel.

A = rng.randint(0,5, (5,1))
 A
array([[2],
       [1],
       [4],
       [2],
       [4]])
A.flatten()
array([2, 1, 4, 2, 4])
A.ravel()
array([2, 1, 4, 2, 4])

So why two functions? I'm not sure, but they do different things behind the scenes. flatten creates a copy, i.e., a new array disconnected from A. ravel creates a view, so a representation of the original array. If you then changed a value after a ravel operation, you would also change it in the original array; if you did this after a flatten operation, you would not.