Complex Data Types in Python 🧩

We've already familiarized ourselves with simple (scalar) data types in Python. Now it's time to move on to more powerful tools — complex (composite) data types, which allow us to store and process collections of values.

What are complex data types? 🤔

Unlike simple data types (numbers, strings, boolean values) that store a single value, complex types can contain multiple values of various types under one name. They are similar to containers or collections where you can organize and store related data.

Python offers four main complex data types:

1. Lists (list) — ordered, mutable collections of elements
2. Tuples (tuple) — ordered, immutable collections of elements
3. Sets (set) — unordered collections of unique elements
4. Dictionaries (dict) — collections of key-value pairs

Let's look at a brief example of each type:

Python 3.13
# List (ordered, mutable)
fruits = ["apple", "banana", "cherry"]

# Tuple (ordered, immutable)
coordinates = (10.5, 20.7, 30.9)

# Set (unordered, unique elements only)
unique_numbers = {1, 2, 3, 4, 5}

# Dictionary (key-value pairs)
person = {"name": "Alex", "age": 30, "city": "New York"}

Comparison of complex data types 📊

Key differences between the main complex data types:

When to use different data types? 🎯

The choice of an appropriate data type depends on the task:

• List (list) — when the order of elements is important and the collection may change

  Python 3.13
  # Daily to-do list
  todo_list = ["Buy groceries", "Walk the dog", "Write code"]
  

• Tuple (tuple) — when data should not change and order is important

  Python 3.13
  # Point coordinates in 3D space
  point_3d = (10.5, 8.3, 9.1)
  

• Set (set) — when you only need unique elements and order doesn't matter

  Python 3.13
  # Unique user names
  unique_users = {"alice", "bob", "charlie"}
  

• Dictionary (dict) — when data is organized as key-value pairs

  Python 3.13
  # User information
  user = {"username": "alex123", "email": "alex@example.com", "active": True}
  

Converting between types 🔄

Python makes it easy to convert data from one complex type to another:

Python 3.13
# Create a list
my_list = [1, 2, 3, 3, 4, 5]

# Convert list to tuple
my_tuple = tuple(my_list)  # (1, 2, 3, 3, 4, 5)

# Convert list to set (duplicates will be removed)
my_set = set(my_list)  # {1, 2, 3, 4, 5}

# Create a dictionary from a list of pairs
pairs = [("a", 1), ("b", 2), ("c", 3)]
my_dict = dict(pairs)  # {"a": 1, "b": 2, "c": 3}

Understanding Check 🧠

Let's check how well you've understood the basic complex data types:

Which data type is best suited for storing unique elements when order doesn't matter?

Conclusion 🎓

Complex data types in Python are powerful tools that allow you to organize, store, and process collections of values.

Choosing the right data type for a specific task can significantly simplify your code and make it more efficient.

In the following articles, we'll examine each of these types in detail, study their methods, and learn how to work with them.

Let's start by exploring lists, the most flexible and frequently used complex data type in Python!