Python provides several built-in data structures that can be used to store and organize data in an efficient manner. These data structures include lists, tuples, sets, and dictionaries.

Lists are one of the most commonly used data structures in Python. They are ordered and mutable, meaning that elements can be added, removed, or modified. Lists can contain elements of different data types and can be accessed using their index. For example, the following code creates a list of integers and accesses the first element:

``````numbers = [1, 2, 3, 4, 5]
print(numbers[0])  # Output: 1
``````

Tuples are similar to lists, but they are immutable, meaning that elements cannot be added, removed, or modified once the tuple is created. They are often used to store a fixed number of related values, such as a point in a Cartesian coordinate system. For example, the following code creates a tuple of two integers representing a point in two-dimensional space:

``````point = (2, 3)
print(point)  # Output: (2, 3)
``````

Sets are unordered collections of unique elements. They are useful for removing duplicate values from a list or for checking if an element is present in a collection. For example, the following code creates a set of integers and checks if the number 3 is present:

``````numbers = {1, 2, 3, 4, 5}
print(3 in numbers)  # Output: True
``````

Dictionaries are collections of key-value pairs, also known as associative arrays or hash maps. They are useful for storing data that needs to be accessed using a unique key. For example, the following code creates a dictionary of names and ages and accesses the age of a person with the name "John":

``````ages = {"John": 25, "Jane": 30, "Bob": 35}
print(ages["John"])  # Output: 25
``````

With Python, you've got several built-in data structures that can be used to store and organize data in an efficient manner. These data structures include lists, tuples, sets, and dictionaries. They are suitable for different types of data and use cases and can be easily used to accomplish various tasks. Understanding how to use these data structures effectively is a key aspect of becoming a proficient Python developer.