8.1.1. Python Data Types

• Numbers: int, float, complex

In Python, numbers can be categorized into three main types: integers, floats, and complex numbers. Integers are whole numbers without decimal points (e.g., -3, 0, 7). Floats, or floating-point numbers, include decimal points (e.g., 3.14, -2.0, 5.99). Complex numbers, which are used in advanced mathematics, have a real part and an imaginary part (e.g., 2 + 3j). Understanding these types is essential for performing calculations and data analysis.

Think of integers as whole apples in a basket, floats as apples that can be sliced into pieces (like 1.5 apples), and complex numbers as a recipe combining apples (the real part) with oranges (the imaginary part) to create a unique fruit salad!

• Strings: Immutable sequences of characters, created using quotes ('Hello' or 'World')

Strings in Python are sequences of characters surrounded by quotes. They can be defined using single quotes (' ') or double quotes (' '). Strings are immutable, meaning once you create a string, you cannot change its content directly. For instance, if you have a string 'Hello', you cannot change it to 'Hello World' directly. Instead, you would need to create a new string.

Imagine writing a word on a piece of paper. Once you write 'Hello', you can't erase just one letter to change it; you must either strike it out and write a new word or start fresh with another piece of paper!

• Booleans: True and False

Booleans are a data type that can hold one of two possible values: True or False. This type is particularly useful in decision-making structures, allowing programmers to evaluate conditions and execute different branches of code based on those conditions. For example, in a light switch, it can either be 'on' (True) or 'off' (False).

Think of a light switch. It only has two states: the light is either on (True) or off (False). This simplicity helps in making foundational decisions in programming.

• Lists: Ordered, mutable collection: [1, 2, 3]

Lists in Python are ordered collections that can store multiple items in a single variable. Lists are mutable, meaning you can change their content after creation, such as adding, removing, or altering items. Lists can contain any data types, including numbers, strings, and even other lists. An example of a list is [1, 2, 3], which contains three integer items.

Imagine you have a grocery bag where you can add or remove items as needed. Just like you can rearrange what’s in your bag, you can modify a list's content at any time.

• Tuples: Ordered, immutable collection: (1, 2, 3)

Tuples are similar to lists in that they are ordered collections of items, but they differ in one crucial aspect: they are immutable. This means once a tuple is created, you cannot change, add, or remove items from it. An example of a tuple is (1, 2, 3). Tuples can be useful when you want to ensure that the data set remains constant and unchanged.

Think of a tuple as a sealed box containing certain items. Once sealed, you cannot add or take out the items, ensuring the contents remain as they were initially packed.

• Dictionaries: Key-value pairs: {'name': 'AI', 'year': 2025}

Dictionaries in Python are collections of key-value pairs. Each key is unique, and it is used to access its corresponding value. For example, in the dictionary {'name': 'AI', 'year': 2025}, 'name' is a key that maps to the value 'AI', and 'year' is a key mapped to the value 2025. This structure allows for efficient retrieval and organization of data.

Consider a phone book. The names (keys) help you find the corresponding phone numbers (values). Just like you quickly look up the number by the person's name, dictionaries allow quick access to their values by their unique keys.

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