Python Data Structures: Lists, Tuples, Dictionaries, and Sets with Practical Code Examples

Data structures are the ways computers store and organize data; in Python, sequences are the most basic structures, where each element is assigned an index.

Python provides several built‑in sequence types: list , tuple , the range function, str (text), binary types ( bytes , bytearray , memoryview ), set / frozenset , and dict . The article walks through each type with examples.

1. List

Lists store mutable elements. Common operations demonstrated include appending, extending, inserting, indexing, slicing, modifying, deleting, and sorting.

In [1]: friends = []
In [2]: friends.append('MissSun')
In [3]: friends
Out[3]: ['MissSun']
In [4]: friends.extend(['Escape', 'Tom'])
In [5]: friends
Out[5]: ['MissSun', 'Escape', 'Tom']
In [20]: friends.insert(1, 'Wang')
In [21]: friends
Out[21]: ['MissSun', 'Wang', 'Escape', 'Tom']
In [22]: len(friends)
Out[22]: 3
In [23]: friends[2]
Out[23]: 'Escape'
In [24]: friends[1:3]
Out[24]: ['Wang', 'Escape']
In [25]: friends[0] = 'MrSun'
In [26]: friends
Out[26]: ['MrSun', 'Wang', 'Escape', 'Tom']
In [27]: friends.remove('Wang')
In [28]: friends
Out[28]: ['MrSun', 'Escape', 'Tom']
In [29]: friends.sort()
In [30]: friends
Out[30]: ['Escape', 'MrSun', 'Tom']

2. Tuple

Tuples store immutable elements and are often used for fixed collections or as dictionary keys. The article shows conversion between lists and tuples, performance comparison, and tuple methods.

In [1]: tup = (1, 2)
In [2]: list(tup)
Out[2]: [1, 2]
In [3]: tuple([1, 2])
Out[3]: (1, 2)
In [4]: import timeit
In [5]: timeit.timeit('["Escape", "MissSun", "Bob", "Lucy"]')
Out[5]: 0.064e-6  # list creation
In [6]: timeit.timeit('("Escape", "MissSun", "Bob", "Lucy")')
Out[6]: 0.0147e-6  # tuple creation (faster)
In [7]: tup = (1, 2, [3, 4])
In [8]: tup[2] += [5, 6]  # raises TypeError but list inside tuple is mutated
Out[8]: (1, 2, [3, 4, 5, 6])

3. Dictionary

Dictionaries store key‑value pairs and preserve insertion order in Python 3. The guide covers creation, element access, updating, deletion, and iteration methods, including legacy iterkeys / itervalues from Python 2.

In [1]: D = {}
In [2]: D[1] = 'a'
In [3]: D['a'] = 1
In [4]: D
Out[4]: {1: 'a', 'a': 1}
In [14]: D.get('a')
Out[14]: 1
In [15]: D.get('b', 2)
Out[15]: 2
In [20]: D.update(b=2, c=3)
In [21]: D
Out[21]: {'a': 1, 'b': 2, 'c': 3}
In [23]: del D['a']
In [24]: D
Out[24]: {'b': 2, 'c': 3}
In [31]: D.keys()
Out[31]: dict_keys(['b', 'c'])
In [32]: D.values()
Out[32]: dict_values([2, 3])
In [33]: D.items()
Out[33]: dict_items([('b', 2), ('c', 3)])

4. Set

Sets store unique, unordered elements. The article demonstrates creation, adding, updating, removing, set algebra (union, intersection, difference), and the immutable frozenset .

In [1]: {1, 2, 3, 1, 1}
Out[1]: {1, 2, 3}
In [2]: s = set()
In [5]: s.add(1)
In [6]: s.add(1)
In [7]: s
Out[7]: {1}
In [9]: s.update([2, 3])
In [10]: s
Out[10]: {1, 2, 3}
In [11]: s.remove(3)
In [12]: s
Out[12]: {1, 2}
In [15]: s.discard(3)  # no error if element absent
In [18]: s1 = set([1, 2, 3])
In [19]: s2 = set([3, 4, 1])
In [20]: s1 & s2
Out[20]: {1, 3}
In [21]: s1 | s2
Out[21]: {1, 2, 3, 4}
In [22]: s1 - s2
Out[22]: {2}
In [24]: fs = frozenset(['a', 'b'])
In [26]: fs.add('c')  # raises AttributeError

The article concludes with a brief disclaimer and links to additional Python resources.