When you first start coding, you can think of Python as a very smart way to keep things in order. Python has to know what “type” of data it is working with in order to store it correctly. For example, you wouldn’t put orange juice in a shoebox or a large dictionary in a glass bottle.
This is the main idea behind Core Concepts of Python Data Types. When you try to take a word away from a number, Python will become confused since the “containers” don’t match. You may make anything from simple calculators to complicated video games if you know how to work with different types of data.
This article will show you the most popular Python Data Types with Syntax Examples and give you easy examples to help you understand them better.
Python Data Types Overview List
Python has several built-in categories for data. Here is a Complete Breakdown of Python Data Types of the most important ones you will use every day.
1. Numeric Types (Numbers)
Numeric data shows you values that you can utilise in maths. There are three primary categories of subtypes:
- Integers (int): These are full numbers that don’t have any decimal points.
- Example: age = 13 or score = -5
- Floating Point (float): These are numbers that have a decimal point in them.
- Example: price = 99.50 or height = 5.8
- Complex Numbers (complex): These are utilised in higher-level arithmetic and science. The imaginary part is indicated with a j.
- Example: z = 3 + 5j
2. Sequence Types (Ordered Collections)
With sequence types, you can keep several things in a certain order.
- Strings (str): These represent text. You must wrap them in single (‘) or double (“) quotes.
- Example: name = “Python”
- Lists (list): A flexible “shopping list” of items. Lists are mutable, meaning you can change them later.
- Example: fruits = [“apple”, “banana”, “cherry”]
- Tuples (tuple): Similar to lists but immutable, meaning they cannot be changed once created.
- Example: coordinates = (10, 20)
3. Mapping and Set Types
- Dictionary (dict): Type of data storage that uses “Key: Value” pairs. For example, you can look up a word (key) to determine its meaning (value).
- Example: user = {“name”: “Alice”, “age”: 12}
- Set (set): A group of things that are all different. It gets rid of any duplicates on its own.
- Example: unique_numbers = {1, 2, 3, 3} (becomes {1, 2, 3})
Python Data Types Cheat Sheet
Here is a short reference table of Python data types, with examples and their primary features.
| Data Type | Keyword | Example | Key Feature |
| Integer | int | 10, -500 | Whole numbers only. |
| Float | float | 3.14, 0.001 | Includes decimal points. |
| String | str | “Hello!” | Text inside quotes. |
| Boolean | bool | True, False | Only two possible values. |
| List | list | [1, 2, 3] | Ordered and changeable. |
| Tuple | tuple | (1, 2, 3) | Ordered but unchangeable. |
| Dictionary | dict | {“a”: 1} | Key-Value pairs. |
Difference Between Mutable and Immutable Python Data Types
When you look at a list of Python data types, one of the most important things to know is the distinction between “Mutable” and “Immutable.”
- Mutable: You can change the data after it is created. Lists, Dictionaries, and Sets are mutable. If you have a list of games and you want to swap one out, you can do that easily.
- Immutable: You cannot change the data. Strings, Integers, and Tuples are immutable. If you want to change a string, Python actually creates a brand-new string instead of modifying the old one.
This distinction is important because immutable types are usually faster for the computer to process, while mutable types give you more flexibility.
Python Data Types with Practical Code Examples
In Python, you don’t have to tell the computer what type of data you are using; it figures it out automatically! This is called “Dynamic Typing.” However, if you ever want to check what type a variable is, you can use the type() function.
Example:
x = 100
print(type(x))
Output: <class ‘int’>
Using type() is a great way to debug your code if something isn’t working as expected.
Type Casting: Changing Python Data Types
Sometimes you need to convert one data type into another. This is called Type Casting. For example, when you get input from a user, Python always treats it as a string (str). If you want to use it for maths, you must convert it to an integer.
- Convert to Int: int(“10”) becomes 10
- Convert to String: str(25) becomes “25”
- Convert to Float: float(5) becomes 5.0
Python Data Types FAQs
- What are the most common python data types for beginners?
The most common types are int (numbers), float (decimals), str (text), and bool (True/False). These four are the building blocks of almost every basic program.
- Can a list have more than one type of Python data?
Yes! A Python list can hold texts, integers, and even additional lists all in one place. For instance, my_list = [“Alice”, 12, 5.5].
- What makes strings unchangeable?
Python can work with strings more quickly in memory since they can’t be changed. This makes sure that the text doesn’t alter by mistake while the application is running.
- What makes a tuple distinct from a list?
A list is a type of data structure that may be changed and employs square brackets. You can’t edit a tuple after you make it, and it needs brackets.
- Is “Boolean” a number type?
Yes, in a technical sense! Python sees True as the number 1 and False as the number 0. That’s why you can occasionally utilise them in maths problems.
Topics Related To Python
🔹 Python Introduction & Fundamentals |
🔹 Functions & Lambda |
🔹 Python for Machine Learning |
🔹 Python for Web Development |
🔹 Python Automation & Scripting |
🔹 Comparisons & Differences |
🔹 Other / Unclassified Python Topics |
| Asyncio – A Guide |