What collections exist in Python and what are their features in terms of speed and memory usage?

In Python, there are several built-in collections (data structures) for storing and organizing data. The main ones:

• list: An ordered, mutable collection that can store elements of different types. Access by index O(1). Insertion/deletion at the end O(1), in the middle/start O(n). Uses more memory than a tuple due to flexibility.
• tuple: An ordered, immutable collection. Faster than lists for iteration and index access O(1). Insertion/deletion is impossible. Uses less memory than a list, as it is fixed.
• set: An unordered, mutable collection of unique elements. Supports set operations (union, intersection, difference). Adding/removing and membership testing O(1) on average. Uses hashing, which may require more memory than list/tuple for small numbers of elements but is efficient for large datasets with unique elements.
• frozenset: An unordered, immutable collection of unique elements. Similar to set, but cannot be changed after creation. Can be used as a key in a dictionary or as an element of another set.
• dict: An unordered, mutable collection of key-value pairs. Keys must be hashable (immutable, e.g., strings, numbers, tuples). Access, addition, and removal by key O(1) on average. Uses hashing, requires more memory than list/tuple but provides fast key-based access.

Collection	Ordered	Mutable	Unique elements	Speed (index/key access)	Speed (insertion/deletion)	Memory usage
list	Yes	Yes	No	O(1)	O(1) (end), O(n) (middle/start)	Average
tuple	Yes	No	No	O(1)	N/A	Less (than list)
set	No	Yes	Yes	N/A	O(1) (average)	Average (for hashing)
frozenset	No	No	Yes	N/A	N/A	Average (for hashing)
dict	No (in older versions, ordered in 3.7+)	Yes	Unique keys	O(1) (average)	O(1) (average)	Larger (for hashing)

# Collection examples

my_list = [1, 2, 'hello'] # List
my_tuple = (1, 2, 'world') # Tuple
my_set = {1, 2, 2, 'hello'} # Set (duplicates removed)
my_frozenset = frozenset({1, 2, 'immutable'}) # Frozenset
my_dict = {'a': 1, 'b': 2} # Dictionary