What are Python's common data structures?

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Python provides a variety of built-in data structures that are essential for organizing and storing data efficiently. Here are the most common ones:

1. List: An ordered, mutable collection that can store elements of different data types. Lists are defined using square brackets [].
   Example: fruits = ["apple", "banana", "cherry"]

2. Tuple: Similar to lists but immutable, meaning their values can't be changed after creation. Defined using parentheses ().
   Example: coordinates = (10, 20)

3. Set: An unordered collection of unique elements. Sets are mutable and defined using curly braces {} or the set() function.
   Example: unique_numbers = {1, 2, 3}

4. Dictionary: A collection of key-value pairs. Dictionaries are mutable and unordered (ordered as of Python 3.7+), defined using curly braces {}.
   Example: person = {"name": "Alice", "age": 30}

5. String: Though technically not a "data structure" in the traditional sense, strings are sequences of characters and behave like immutable lists.
   Example: message = "Hello, World!"

6. Arrays (via the array module or NumPy): Useful for numeric data and more memory-efficient than lists for large datasets.

7. Deque (from collections module): A double-ended queue allowing fast appends/pops from both ends.

These structures are widely used in various applications for their flexibility, efficiency, and ease of use.

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