Comparing Collections - Practical Programming, Fourth Edition - Dmitry Zinoviev - RutLib.com

Comparing Collections

You’ve now seen strings, lists, sets, tuples, and dictionaries. They all have their uses. Here is a table comparing them:

Collection	Mutable?	Ordered?	Use When…
str	No	Yes	You want to keep track of text.
list	Yes	Yes	You want to keep track of an ordered sequence that you want to update.
tuple	No	Yes	You want to build an ordered sequence that you know won’t change or that you want to use as a key in a dictionary or as a value in a set.
set	Yes	No	You want to keep track of values, but order doesn’t matter, and you don’t want to keep duplicates. The values must be immutable.
dictionary	Yes	No	You want to keep a mapping of keys to values. The keys must be immutable.

Hash tables are one of the most important data structures in computer science. They allow programs to store and retrieve data in constant time on average, regardless of the amount of data. This speed is made possible by a process called hashing, which converts a key (like a string or number) into an index that tells the computer where to find the corresponding value in memory.

Python dictionary is a data type that uses a hash table under the hood. When you write my_dict[’name’] = ’Alice’, Python hashes the key ’name’ to determine where to store ’Alice’. Later, it uses the same hash to retrieve the value quickly. Thanks to hash tables, Python dictionaries are incredibly fast for operations like lookups, insertions, and deletions, making them a go-to tool for many programming tasks.