Let’s talk about dictionaries in Python the unsung heroes of data storage. Unlike lists and tuples, dictionaries allow us to store data using unique identifiers (keys) instead of index positions. This means that we can easily access specific items without having to remember their position within a list or tuple. Here’s what you need to know:
– Dictionaries use key-value pairs to organize information, which makes them great for storing data based on unique identifiers (like names).

– Accessing dictionary items using keys is incredibly efficient since they use hash tables internally. This means that we can quickly and easily retrieve the value associated with any given key without having to iterate through an entire list or tuple.
– Dictionaries also allow us to modify our data in place, which can be much more efficient than other Python data types for certain use cases. But there are some drawbacks:
– Unlike lists and tuples, dictionaries do not have a fixed order or indexing system. This means that we cannot easily iterate through all of the keys in our dictionary (or access them by their position).
– Dictionaries also require more memory than other Python data types since they use hash tables internally to store and retrieve key-value pairs.

If you need even more control over your dictionary data, consider subclassing the built-in `dict` class or using a third-party library like `collections. UserDict`. This can be incredibly powerful for creating complex data structures that are tailored specifically to your needs. But before we dive into those more advanced techniques, let’s take a quick break and talk about some of the benefits (and drawbacks) of using dictionaries vs. other Python data types like lists or tuples.

Benefits:
– Dictionaries allow us to store data in key-value pairs, which can be incredibly useful for organizing information based on unique identifiers (like names). – Dictionaries are highly efficient when it comes to accessing specific items using their keys, since they use a hash table internally. This means that we can quickly and easily retrieve the value associated with any given key without having to iterate through an entire list or tuple.

– Dictionaries also allow us to modify our data in place (i.e., by updating existing values rather than creating new items), which can be much more efficient than using other Python data types for certain use cases. Drawbacks:
– Unlike lists and tuples, dictionaries do not have a fixed order or indexing system.

This means that we cannot easily iterate through all of the keys in our dictionary (or access them by their position).

– Dictionaries also require more memory than other Python data types since they use hash tables internally to store and retrieve key-value pairs. Now how to create custom dictionary classes using Python’s built-in `dict` class. This can be incredibly powerful for creating complex data structures that are tailored specifically to your needs, but it also comes with some important considerations and best practices:

1. Define a new class called MyDict that subclasses the built-in dict class (i.e., `class MyDict(dict):`).

This allows us to inherit all of the functionality from Python’s existing dictionary implementation while adding our own custom methods or constraints as needed. 2. Override any necessary methods in your new class, such as `__init__()`, `__getitem__()`, and `__setitem__()`. These are called automatically when we create a new instance of the MyDict class (i.e., `my_dict = MyDict()`). 3. Use the `super().__init__()` method to call the parent dict constructor, which ensures that our custom dictionary class still behaves like a regular Python dictionary in most cases.

4. Add any additional functionality or constraints as needed using your own methods and properties within the new MyDict class. For example, you might add a `validate_key()` method to ensure that all keys meet certain criteria (like being numeric or having a specific format). 5. Test your custom dictionary implementation thoroughly to ensure that it behaves correctly in all cases. This can involve writing unit tests using Python’s built-in unittest module, as well as testing with real data and use cases to ensure that everything works as expected.