Python, a versatile and powerful programming language, offers a rich set of built-in data structures that are both intuitive and efficient. These data structures form the building blocks for designing more complex data structures and algorithms, and they play a pivotal role in enhancing the efficiency of Python programs. Whether you're dealing with data storage, retrieval, or manipulation, understanding Python's data structures is crucial for any aspiring Python developer.

Big O notation describes the upper limit of an algorithm's runtime as the size of its input grows.

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A dynamic array that can store elements of any type. It supports indexing, slicing, and various methods to manipulate the data.
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Similar to a list but immutable. Once you create a tuple, you cannot alter its contents.
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An unordered collection of unique elements. Useful for membership tests and eliminating duplicate entries.
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An unordered collection of key-value pairs. It allows you to store and retrieve data based on a key.
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A double-ended queue that supports adding and removing elements from both ends in O(1) time.
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A Last-In-First-Out (LIFO) data structure. Python doesn't have a built-in stack, but it can be easily implemented using lists or collections.deque.
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A First-In-First-Out (FIFO) data structure. Python's queue module provides several implementations, including Queue , LifoQueue, and PriorityQueue.
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