4. Data Collections

Python’s data collections are the built‑in container types you use to store, organize, and operate on groups of values. The four main collection types are: list, tuple, dictionary, and set.

Also, strings are considered a sequence type (a kind of collection) because they behave like collections of characters, although conceptually, strings are considered primitives, not collections, in many languages other than Python.

range, is debatable as a “collection”; it’s really a lazy sequence generator, not a traditional data structure. It does like a sequence, so Python treats it as one. Note that range stores a formula, not data; it computes values on-demand. Therefore, range is a sequence protocol implementer, not a data container.

4.1. Collection Types

Here is a summary of the commonly used data collection types.

Type	Literal	Mutable	Ordered	Usage
list	[1, 2, 3]	Yes	Yes	General, grow/shrink, index/slice.
tuple	(1, 2, 3)	No	Yes	Fixed records; hashable if elements are.
dict	{"a": 1, "b": 2}	Yes (3.7+)	Yes	Key-value mapping.
set	{1, 2, 3}	Yes	No	Unique items, math set ops.
frozenset	frozenset({1,2})	No	No	Hashable set (e.g., dict keys).
range	range(10)	No	Yes	Lazy integer sequence.
str	"hi"	No	Yes	Text

4.2. Sequence Types

There are three basic sequence types: list, tuple, and range objects.

*Since Python 3.7+, dictionaries preserve insertion order, but logically unordered.

Python data collections let you: - model data naturally (ordered records, lookup tables, unique elements), - support iteration and common algorithms, - use methods for adding, removing, searching, and transforming data.

Fig. 4.1 10 Data Structures You Use Daily (bytebytego )