How are `dict` dictionaries structured and which operations are fast?

Dictionaries in Python are implemented as high-performance hash tables, which ensures exceptional data processing speed under certain conditions. Understanding the internal structure of this data type allows developers to write optimized code capable of efficiently processing large volumes of information.

Thanks to the hash table structure, basic dictionary operations such as accessing a value by key, inserting a new pair, and deleting an existing one are performed in constant time on average. This makes dictionaries indispensable tools for fast data lookup by unique identifiers, regardless of the overall collection size.

In order for a key to be efficiently found in the hash table, it must be hashable, meaning it must be an immutable object. Strings, numbers, boolean values, and tuples containing only immutable data types are well-suited as keys, whereas lists and dictionaries cannot be used in this role.

Starting with Python 3.7, dictionaries are guaranteed to preserve the original insertion order of elements, which was long a feature of the third-party `OrderedDict` implementation. This functionality is now part of the official language specification, simplifying the logic for working with ordered data.

To efficiently solve common tasks, such as counting the frequency of elements, developers do not need to write custom logic using dictionaries. Instead, they should use the specialized `Counter` class from the built-in `collections` module, which is optimized for such operations and provides a convenient interface.