For this assignment, you will be using Python classes to create a new object that behaves like a dictionary in Python, but you are not allowed to use a built-in dictionary in your implementation. The purpose is to gain a better understanding of how dictionaries function under the hood by creating your own.

This assignment focuses on creating a data structure that can be used in other programs rather than building a concrete, real-world application. Customized data structures go together with Python naturally since Python is an object-oriented language.

Be sure to review this entire README before you get started. There are a lot of guidelines and hints to help you if you get stuck. Please resist the urge to 'skim'.

You will create a single class named NoDict that performs the primary functions of a dictionary:

• Association of key/value pairs
• Ability to insert a new key/value entry or update an existing entry
• Ensure that all keys are unique (and handle duplicates)
• Look up a value when given a key
• Delete a key/value entry

• Understand how dictionaries work under the hood
• Learn what hashing is and how to apply it
• Understand what a hash map is
• Apply principles of object composition to a solution

We will use the principle of object composition to create the NoDict class. This means that the primary NoDict class will be composed of a collection of smaller class objects called Nodes. Each Node object represents a single key/value pair. The keys and values of a dictionary should be kept together, otherwise the benefit of associative mapping that dictionaries provide becomes lost. The job of the NoDict class is to manage the Nodes and provide interface methods that can be easily used by a programmer. The Node class itself is not meant to be used directly by a programmer, but it is a private working component of the NoDict class.

• Define a class named Node that can be initialized with a key (mandatory) and a value (optional). Example:

  n1 = Node("Kevin")  # Create a Node with a key, but no value
  n2 = Node("George", 21)  # Create Node with key and value

• The key and the value should be stored as instance variables within Node.
• Within the Node class, define and implement python "dunder" methods for __init__, __repr__, and __eq__
• The Node class should print a human-readable representation of its key/value contents when asked. The __repr__ method can do this. For example, this is not very readable

  >>> print(Node("Kevin", 21))
  <__main__.Node object at 0x7f4b24f33580>

  The following, however, is more readable and shows the contents of the Node. It also adheres to the rules about what to return from a __repr__ method vs. a __str__ method. The __repr__ method should return a string representation of a Python object that may be evaluated by the Python interpreter to instantiate another instance of the object.

  >>> print(Node("Kevin", 21))
  Node("Kevin", 21)

  Implement the __repr__ method like this:

  return f'{self.__class__.__name__}({self.key}, {self.value})'

• The Node class should hash its own key, and keep that hash value as an instance attribute, self.hash. This hash value will be used by the NoDict class. Use the built-in Python hash() function for this.
• The Node class object should be able to compare itself to other Node objects using the Python built-in == operator. For example

  n1 = Node('Mike', 21)
  n2 = Node('Mike', 34)
  n3 = Node('Nick', 56)
  print(f'n1 == n2 ? {n1 == n2}')
  print(f'n2 == n3 ? {n2 == n3}')

  This should output

  n1 == n2 ? True
  n2 == n3 ? False

  To do make this possible, implement the __eq__ method within the Node class.
• Each method defined in the Node class should have a docstring.

Create a class named NoDict which implements the key features of a dictionary. Do not use the dict keyword, {} syntax, or other Python dictionary derivatives such as OrderedDict or defaultdict in your implementation.

Your NoDict class should initialize with an arbitrary default size of 10 internal 'buckets', but can be overridden for more or fewer buckets. The buckets should be implemented as a list of lists. Each bucket will contain 0 or more Node objects. Please review how to initialize a list containing n empty lists.

[
   [], [], [], [] ... []  # n empty lists, contained in a list
]

The buckets are the important part of the NoDict class — they are where all the key/value Nodes will be stored. The NoDict class should implement the following class methods:

• __init__ - class initializer to create the buckets according to a size parameter. Save the size parameter as an instance variable in the class. Create another instance variable to hold the bucket list. Your instance variable should be named self.buckets.

• __repr__ - string representation of the contents of the buckets. The __repr__ dunder method will be called any time you print the dictionary. It will give a detailed view of everything, to help you in debugging. You may notice that this __repr__ method does not strictly adhere to the same rule that we used for the Node object, which is okay for this example problem because we want to see all the buckets. Use the following code snippet for this method:

      def __repr__(self):
         """Return a string representing the NoDict contents."""
         # We want to show all the buckets vertically
         return '\n'.join([f'{self.__class__.__name__}.{i}:{bucket}' for i, bucket in enumerate(self.buckets)])

• add - This class method should accept a new key and value, and store it into the NoDict instance. However, this method should not allow duplicate keys. First, make a Node class using the key and value, e.g. new_node = Node(key, value).

  To add the Node into a bucket, you must first determine which bucket to use. Get the previously hashed value of the Node from its .hash attribute which you computed, and modulo-divide (%)that large integer down to an index that can be used to reference any one of the buckets. That means you will modulo-divide the Node's hash value by the number of buckets in NoDict.

  Once you have a bucket index, you can reference the bucket itself (recall that each bucket is a list). Now that you have the bucket selected, you must iterate through its contents (which are all Node instances). As you examine each Node instance in the bucket, you should test for equality with the Node instance that you are trying to insert. If that Node does not match any existing Node in the bucket, append it to the bucket. If a match is found (the Node already exists in the bucket), then remove the previous matching Node before appending the new one. This way you have solved the 'No duplicates' requirement.

• get - This class method should perform a key-lookup in the NoDict class. It should accept just one parameter: The key to look up. If the key is found in the NoDict class, return its associated value. If the key is not found, raise a KeyError exception.

  This method will look similar to add. First, create a Node instance from the key, e.g., node_to_find = Node(key). Then compute the bucket index the same way you did for the add function. Once you have the bucket, iterate through the bucket and look for a Node that matches node_to_find. If you find it, return that Node's value. If the iteration completes without finding a matching Node, raise a KeyError exception like this:

  raise KeyError(f'{key} not found')

• __setitem__ - Implement this magic "dunder" method within the NoDict class to enable square-bracket assignment behavior. Think of it like a setter method. After enabling this behavior, you will be able to do this:

     my_dict = NoDict()
     my_dict['Kevin'] = 21

• __getitem__ - Implement this magic "dunder" method within the NoDict class to enable square-bracket reading behavior. This will make the class behave more like a regular dictionary. Without enabling this behavior, you could not write an expression like this:

  kevin_age = my_dict['Kevin']

At this point, you have defined a very basic NoDict data structure that functions as an associative dictionary that can store and retrieve key/value pairs. You have used object composition by declaring a Node class to represent a hashed associative binding, and then used those Nodes within the NoDict class. You have have also uncovered the secret of why dictionaries perform at close to ideal O(1) (constant time) lookup speed: Instead of iterating over a giant list of nodes, you are using the hash function to directly compute the bucket index of where to find a node.

By now you should be familiar with how to run the tests that come with assignments. Here are a couple of options for testing:

• Use the built-in VSCode "Test Tube" extension by searching the command pallette for Python: Configure Tests. Choose "unittest" as your framework, and "tests" as the folder containing the tests, and test*.py as the file pattern. The test tube should appear in your Navigator bar.
• Use the command line : python -m unittest tests/test_nodict.py

Make sure all of the provided tests are passing before submitting your pull request.

• The Mighty Dictionary - Brandon Craig Rhodes
• High Performance Python - Micha Gorelick, Ian Ozsvald

2. Clone your own repo to your local development machine.
3. Create a separate branch named dev and checkout the branch.
4. Commit your changes, then git push the branch back to your own GitHub account.
5. From your own GitHub repo, create a pull request (PR) from your dev branch back to your master branch.
6. Copy/Paste the URL link to your PR as your assignment submission.
7. Your grader will post code review comments inline within your pull request in your GitHub account. Be sure to respond to any comments and make requested changes. RESUBMIT a new link to your PR after making changes. This is the code review iteration cycle.