Structured data, Python Programming

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:


644_structured data.png



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]

>>> a[2] 

>>> a[-1] 

>>> 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.



847_structured data  2.png



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, [], [[]]]




515_structured data  3.png


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])


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



Posted Date: 8/11/2012 1:38:38 AM | Location : United States

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