In the prior example, we used a dictionary to describe a hypothetical person, with three keys. Suppose, though, that the information is more complex. Perhaps we need to record a first name and a last name, along with multiple job titles. This leads to another application of Python’s object nesting in action. The following dictionary, coded all at once as a literal, captures more structured information:

  >>> rec = {'name': {'first': 'Bob', 'last': 'Smith'}, 
            'job': ['dev', 'mgr'],
            'age': 40.5}

Here, we again have a three-key dictionary at the top (keys “name,” “job,” and “age”), but the values have become more complex: a nested dictionary for the name to support multiple parts, and a nested list for the job to support multiple roles and future expansion. We can access the components of this structure much as we did for our matrix earlier, but this time some of our indexes are dictionary keys, not list offsets:

  >>> rec['name']                  # 'Name' is a nested dictionar y
  {'last': 'Smith', 'first': 'Bob' }

  >>> rec['name']['last']          # Index the nested dictionar y
  'Smith '

  >>> rec['job']                   # 'Job' is a nested lis t
  ['dev', 'mgr' ]

  >>> rec['job'][-1]               # Index the nested lis t
  'mgr '

  >>> rec['job'].append('janitor') # Expand Bob's job description in-place
  >>> rec
  {'age': 40.5, 'job': ['dev', 'mgr', 'janitor'], 'name': {'last': 'Smith', 'first':
  'Bob'}}

Notice how the last operation here expands the nested job list—because the job list is a separate piece of memory from the dictionary that contains it, it can grow and shrink freely (object memory layout will be discussed further later in this book).

The real reason for showing you this example is to demonstrate the flexibility of Python’s core data types. As you can see, nesting allows us to build up complex information structures directly and easily. Building a similar structure in a low-level language like C would be tedious and require much more code: we would have to lay out and declare structures and arrays, fill out values, link everything together, and so on. In Python, this is all automatic—running the expression creates the entire nested object structure for us. In fact, this is one of the main benefits of scripting languages like Python.

Just as importantly, in a lower-level language, we would have to be careful to clean up all of the object’s space when we no longer need it. In Python, when we lose the last reference to object—by assigning its variable to something else, for example—all of the memory space occupied by that object’s structure is automatically cleaned up for us:

  >>> rec = 0                # Now the object's space is reclaimed

Technically speaking, Python has a feature known as garbage collection that cleans up unused memory as your program runs and frees you from having to manage such details in your code. In Python, the space is reclaimed immediately, as soon as the last reference to an object is removed. We’ll study how this works later in this book; for now, it’s enough to know that you can use objects freely, without worrying about creating their space or cleaning up as you go.*