Table of Contents
List: Flexible Ordered Collection
Tuple: Lightweight immutable container
Dictionary: Efficient key-value pair structure
Collection: Deduplication and relational operations
Home Backend Development Python Tutorial Working with Data Structures: Lists, Tuples, Dictionaries, Sets in Python

Working with Data Structures: Lists, Tuples, Dictionaries, Sets in Python

Jul 07, 2025 am 12:15 AM

The most commonly used data structures in Python are lists, tuples, dictionaries, and collections. 1. The list is mutable and orderly, suitable for storing content that needs to be frequently modified, and supports operations such as adding, inserting and deleting elements; 2. Tuples are immutable, suitable for data sets that will not change, have better performance and can be used as keys for dictionaries; 3. The dictionary stores data in key-value pairs, has high search efficiency, and is suitable for fast retrieval scenarios; 4. The set is used for deduplication and set operations, and has efficient member detection capabilities. Mastering their characteristics and applicable scenarios can improve code efficiency and clarity.

Working with Data Structures: Lists, Tuples, Dictionaries, Sets in Python

In Python programming, mastering basic data structures is the first step in writing good code. List, Tuple, Dictionary and Set are the four most commonly used data structures. They each have their own characteristics and are suitable for different scenarios.

Working with Data Structures: Lists, Tuples, Dictionaries, Sets in Python

List: Flexible Ordered Collection

Lists are one of the most commonly used and flexible data structures in Python. It is a variable, ordered collection of elements suitable for storing content that needs to be modified frequently.

Working with Data Structures: Lists, Tuples, Dictionaries, Sets in Python
  • Lists are a great choice if you need to add or delete elements dynamically.
  • For example, when recording the history entered by the user, you can use append() to continuously add new content.
  • Common operations include:
    • list.append(x) add element to the end
    • list.insert(i, x) inserts elements at the specified position
    • list.pop(i) deletes and returns the element at the specified location

However, it should be noted that the efficiency of inserting or deleting lists in the middle is low. If the data volume is large and the intermediate position is frequently operated, other structures may be considered.


Tuple: Lightweight immutable container

Tuples are very similar to lists, but they are immutable and cannot be modified once created. This feature makes it "lighter" than lists and is more suitable for data collections that do not change.

Working with Data Structures: Lists, Tuples, Dictionaries, Sets in Python
  • For example, when a function returns multiple values, the default return is a tuple.
  • Because it is immutable, tuples can be used as keys to dictionaries (as long as the elements inside are hashable), while lists cannot.
  • The creation method is simple, for example: t = (1, 2, 3) or t = 1, 2, 3

If you have a dataset that does not need to be modified, using tuples will be safer and can improve performance.


Dictionary: Efficient key-value pair structure

Dictionary is one of the most powerful data structures in Python. It stores data in the form of key-value pairs , and is very efficient in finding it.

  • For example, if you want to quickly find user information based on your username, it is very suitable to use a dictionary.
  • Common operations:
    • d[key] = value Add or update key value
    • d.get(key) gets the value to avoid errors when the key does not exist
    • d.items() traverses all key-value pairs

It should be noted that the keys of the dictionary must be of immutable type (such as strings, numbers, tuples), and the value can be of any type.


Collection: Deduplication and relational operations

A set is an unordered and non-repetitive set of elements, suitable for deduplication, intersecting and difference set operations.

  • For example, if you have a user access log, you can do it by simply converting it into a collection.
  • Common operations:
    • set.add(x) Add element
    • set.remove(x) deletes the element (if it does not exist, an error will be reported)
    • a | b union, a & b intersection, a - b difference set

The collection is implemented using a hash table internally, so it is very fast to determine whether an element is included.


Basically that's it. These four data structures are often used in actual development. Understanding their characteristics and applicable scenarios can allow you to write clearer and more efficient Python code.

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