One of the most common situations you will encounter when working with data is transforming a list into an array.

When operating such a transformation, it is important to consider the objects the lists contain because this will determine the dimensionality and the dtype of the resulting array.

Let's start with this first example of a list containing just integers:

In: import numpy as np

In: # Transform a list into a uni-dimensional array
    list_of_ints = [1,2,3]
    Array_1 = np.array(list_of_ints)

In: Array_1

Out: array([1, 2, 3]) 

Remember that you can access a one-dimensional array as you would with a standard Python list (the indexing starts from zero):

In: Array_1[1] # let's output the second value

Out: 2  

We can ask for further information about the type of the object and the type of its elements (the effectively resulting type depends on whether your system is 32-bit or 64-bit):

In: type(Array_1)

Out: numpy.ndarray

In: Array_1.dtype

Out: dtype('int64')  

Our simple list of integers will turn into a one-dimensional array; that is, a vector of 32-bit integers (ranging from -231 to 231-1, the default integer on the platform we used for our examples).