Introduction to Python Data Types

Welcome to the foundational concept of data types in Python! Everything you work with in Python—from simple numbers to complex data structures—has a “type.” A data type is like a category that tells Python what kind of data a variable holds and what you can do with it.

Think of it like this: in the real world, you know that you can do math with numbers (like 2 + 2), but you can’t do math with a word (like “hello” + “world” in a mathematical sense). Python needs to know these same rules, and data types are how it does it.

Why are Data Types Important?

Understanding data types is fundamental for several reasons:

Defines Operations: The data type of an object determines which operations (or functions) you can use with it. For example, you can divide numbers, but you can’t divide strings.
Memory Allocation: Python’s interpreter uses data types to figure out how much memory to allocate for a variable. An integer might take up less space than a long, detailed string.
Error Prevention: Knowing the types helps you avoid errors. If you try to perform an operation that isn’t allowed for a certain data type, Python will raise a TypeError.

Python’s Core Data Types

Python comes with a rich set of built-in data types. Here’s a high-level overview, which we will explore in detail in the upcoming sections.

Text Type: str

Description: Used for storing textual data, like words and sentences. Strings are sequences of Unicode characters.
Example: "Hello, Python!", 'Python is fun'

Numeric Types: int, float, complex

int: For whole numbers, positive or negative, without decimals. Example: 100, -50.
float: For numbers with a decimal point. Example: 3.14, -0.01.
complex: For complex numbers with a real and imaginary part. Example: 2 + 3j.

Sequence Types: list, tuple, range

list: A mutable (changeable) collection of items in a specific order. Example: [1, "apple", 3.5].
tuple: An immutable (unchangeable) collection of items in a specific order. Example: (1, "apple", 3.5).
range: Represents an immutable sequence of numbers, commonly used for looping. Example: range(6).

Mapping Type: dict

Description: An unordered collection of key-value pairs. Dictionaries are mutable and are used to store related data.
Example: {"name": "Alice", "age": 25}

Set Types: set, frozenset

set: An unordered collection of unique items. Sets are mutable. Example: {"apple", "banana", "cherry"}.
frozenset: An immutable version of a set.

Boolean Type: bool

Description: Represents one of two values: True or False. It’s the basis for all comparisons and control flow.
Example: is_active = True

Binary Types: bytes, bytearray, memoryview

Description: Used to handle binary data (raw bytes).

Checking the Type of a Variable

You can easily find out the data type of any variable in Python using the built-in type() function. This is incredibly useful for debugging and understanding how your code is working.

Pyground

Let's see the `type()` function in action. What are the data types of the following variables?

Expected Output:


The type of 'a' is: <class 'int'>
The type of 'b' is: <class 'str'>
The type of 'c' is: <class 'float'>
The type of 'd' is: <class 'list'>
The type of 'e' is: <class 'dict'>
The type of 'f' is: <class 'bool'>

Output:

In Python, you don’t need to explicitly declare the data type of a variable. The type is automatically assigned when you give the variable a value. We’ll cover this concept, known as Dynamic Typing, in the next section.

Data Types Overview Dynamic Typing