Python Programming

The main star of NumPy is the np.ndarray object. It stands for N-dimensional arrays. You can also use the shorter np.array as an alias (we will use this for our lesson!)

NumPy is all about representing your vectors/matrices as np.array instances, and using the operators and methods of the object to transform array instances. That’s it really!

The key knowledge you need to figure out is what operators, attributes and methods are available for a np.array instance, and how to use them! And also all the many functions provided by NumPy to help you manipulate the np.arrays.

np.array versus list

Why do we need np.arrays when we have Python lists?

Well, np.arrays are more efficient, and these often have smaller memory consumption and better runtime compared to Python lists. So definitely use np.ndarray over list any time for large scale array/matrix operations.

You should also treat np.array as something completely different from Python lists. How you use np.array instances will be quite different from lists! So don’t start using list methods with np.arrays - it will probably not work!

You can create a new np.array instance by passing in a list (or any other sequence) as an argument to the constructor.

Conversely, use the .tolist() method of np.array to convert the NumPy array to a list.

>>> x = np.array([[1, 2, 3], [4, 5, 6]]) 
>>> print(x)
[[1 2 3]
 [4 5 6]]
>>> print(type(x))
<class 'numpy.ndarray'>
>>> x_list = x.tolist()
>>> print(x_list)
[[1, 2, 3], [4, 5, 6]]
>>> print(type(x_list))
<class 'list'>