Structured data

We will often  want  to work  with  large  groups of data.   Rather  than  providing  each  number its own  value,  we need  to manage the data  into natural structures: grocery  lists, matrices, groups of employee medical records. We will explain  a simple  but  enormously useful  and ?exible list, which is conveniently create into Python: the list. The precise information  of how lists are shown inside a computer vary from language to language. We will take an abstract model  in which we think of a list as an ordered sequence of memory locations that numbers. So, for example, in Python, we may show a list of three integers as:

Now that we have lists, we have some new kinds  of statements, which let us extract elements of a list by specifying their indices.   An index  of 0 related to the ?rst  element of a list.  An index  of -1 related to the last element So, if a is bound as above, then we would have:

>>> a[0]

2 

>>> a[2] 

9 

>>> a[-1] 

9 

>>> a[3] 

Traceback (most recent call last): File "", line 1, in IndexError: list index out of range.Note that if we attempt to access an element of the list that is not present, then a exception is generated.

It is also possible  to have  an empty list, which  is given in Python as []. We will show it in our memory diagrams as a short box. So example, this list  looks like this in memory:

>>> z = [3, [], [[]]]

Python has a useful method, len, which takes a list as an argument and gives its size. It does not look inside  the elements of the list-it just returns the number of elements at the top level of list. So, we take

>>> len([1, 2, 3])

3

>>> len([[1, 2, 3]])

1