Python has built-in data structures like lists, tuples, sets, and dictionaries. Use lists for ordered collections, sets for unique items, and dictionaries for key-value pairs.

Python offers several built-in data structures that are optimized for different use cases, allowing developers to choose the right structure based on their needs. The most common built-in data structures include lists, tuples, sets, and dictionaries.

• Lists: Lists are ordered collections that can contain duplicate elements. They are mutable, meaning you can change their content after creation. Lists are ideal for situations where you need to maintain the order of elements and perform operations like appending or removing items. For example, if you're managing a collection of user inputs, a list would be a suitable choice.

• Tuples: Tuples are similar to lists but are immutable, meaning their content cannot be changed once created. This makes tuples useful for fixed collections of items that should not change, such as coordinates or RGB color values. They also have a smaller memory footprint compared to lists, making them a better choice for performance-sensitive applications.

• Sets: Sets are unordered collections of unique elements, ideal for membership testing and eliminating duplicate entries. They are mutable and support operations like union, intersection, and difference, making them useful for tasks that involve group comparisons, such as finding common users between two datasets.

• Dictionaries: Dictionaries are key-value pairs that allow for fast lookups based on keys. They are unordered and mutable, making them ideal for situations where you need to associate unique keys with specific values, such as managing user profiles or configuration settings. By understanding the strengths and appropriate use cases for each of these built-in data structures, you can make more informed decisions in your Python programming.