Author Dusty Phillips Reviewers

>>> ds = DictSorted()
>>> d = {}
>>> ds['a'] = 1
>>> ds['b'] = 2
>>> ds.setdefault('c', 3)
3
>>> d['a'] = 1
>>> d['b'] = 2
>>> d.setdefault('c', 3)
3
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Python Data Structures
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 182 
]
>>> for k,v in ds.items():
... print(k,v)
...
a 1
b 2
c 3
>>> for k,v in d.items():
... print(k,v)
...
a 1
c 3
b 2
Ah, our dictionary is 
sorted and the normal dictionary is not. Hurray!
If you wanted to use this class in production, you'd have to override 
several other special methods to ensure the keys are up to date in all 
cases. However, you don't need to do this; the functionality this class 
provides is already available in Python, using the 
OrderedDict
object in 
the 
collections
module. Try importing the class from 
collections
, 
and use 
help(OrderedDict)
to find out more about it.
Queues
Queues are peculiar data structures because, like sets, their functionality can
be handled entirely using lists. However, while lists are extremely versatile
general-purpose tools, they are occasionally not the most efficient data structure for 
container operations. If your program is using a small dataset (up to hundreds or even 
thousands of elements on today's processors), then lists will probably cover all your 
use cases. However, if you need to scale your data into the millions, you may need a 
more efficient container for your particular use case. Python therefore provides three 
types of queue data structures, depending on what kind of access you are looking for. 
All three utilize the same API, but differ in both behavior and data structure.
Before we start our queues, however, consider the trusty list data structure.
Python lists are the most advantageous data structure for many use cases:
• 
They support efficient random access to any element in the list
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Chapter 6