Are you confused about why your Python code keeps throwing type errors? Or why do some variables behave unpredictably while others don’t change at all? The real issue lies in not fully understanding Python data types.
Whether you’re just starting out or diving into a data science course, skipping the basics can cost hours of debugging. This post explains what are data types in python, explains mutability, and helps you use each type the right way so your programs run smoothly and efficiently.
What are Data Types in Python?
Python is a programming language that you can easily use for many different tasks.
If you mess this part up, your program might crash, behave unexpectedly, or just slow you down.
Why you need to know this:
· You can prevent errors by choosing the right type.
· It helps improve your code efficiency.
· Data types make debugging faster.
· Crucial for interviews, especially if you’re applying after a data science course.
· You must understand them to master control flow, loops, or even basic functions.
Understanding Python’s Built-In Data Types
Python became popular because it is simple to use and easy to read. That’s why both beginners and experienced people like using it.
Even in 2025, this is one big reason people choose Python. In data science, people come from many backgrounds. Some are good with numbers, and others know coding well. Python makes it easier for anyone to start learning and building things without much trouble.
Python offers several built-in data types grouped into categories:
1. Numeric Types
int: For integers (e.g., 1, 500)
float: For decimals (e.g., 10.5, 3.14)
complex: For complex numbers (e.g., 4 + 3j)
All of these are immutable data types in python, meaning their value cannot be changed after assignment.
2. Text Type
str: For text or string values (e.g., ‘hello,’ “world”)
Again, strings are immutable. Even if you change a character, python creates a new string object behind the scenes.
3. Boolean Type
Bool: Can be either True or False. Often used in condition checks and logical operations.
Mutable vs Immutable Data Types in Python
Let’s clarify something: many beginners struggle with mutability.
In simple terms:
Ø Immutable means you can’t change the value once it’s set.
Ø Mutable data types in python mean you can change the contents without creating a new object.
Here’s a breakdown:
Python Data Types: Mutable vs Immutable
| Data Type | Can You Change It? (Mutability) | What It’s Usually Used For |
| int | No (Immutable) | Storing whole numbers like age, count, or price |
| float | No (Immutable) | Dealing with decimal values, such as weight or salary |
| str | No (Immutable) | Handling text, names, messages, or any string data |
| tuple | No (Immutable) | Storing a fixed set of items like coordinates or settings |
| list | Yes (Mutable) | Holding a collection of items that may change, like a to-do list |
| set | Yes (Mutable) | Keeping unique items without duplicates, like tags or categories |
| dict | Yes (Mutable) | Pairing keys with values, like name and phone number in a contact list |
This comparison will help you avoid mistakes like modifying immutable types or incorrectly assuming you can change them, like lists or dictionaries.
The Different Collection Data Types
1. List – Mutable and Ordered
A list is like a shopping list.
You can add, remove, or change items.
my_list = [1, 2, 3]
my_list.append(4) # This changes the original list
Used when your data will change. Lists are mutable data types in python.
2. Tuple – Immutable and Ordered
Tuples are similar to lists but fixed.
my_tuple = (1, 2, 3)
Useful for data that shouldn’t be changed, like coordinates.
3. Set – Mutable and Unordered
Sets store unique values. Great for removing duplicates.
my_set = {1, 2, 2, 3}
print(my_set) # Output: {1, 2, 3}
4. Dictionary – Mutable and Key-Value Based
Dictionaries are like real-life dictionaries. They pair a word (key) with a definition (value).
my_dict = {“name”: “Rahul”, “age”: 25}
my_dict[“age”] = 26
Very useful in data science course work for structuring data.
How Python Treats Mutability
We visualised the number of common mutable vs. immutable types. This helps you mentally group what’s safe to modify and what’s not. Check the chart for clarity.
Type Conversion in Python
You’ll often need to convert one data type into another:
· str() to convert to string
· int() to convert to integer
· float() for decimal numbers
· list() or tuple() for collection types
Why is this important?
Because mismatched types lead to TypeErrors. You must know which conversions are safe.
Real-world Example: Why Data Types Matter in Data Science
In your data science course, imagine you’re working with a CSV file. If the “age” column comes in as strings (‘25’, ‘30’), you can’t calculate averages. You’ll have to convert it to integers or floats.
Not knowing what data types are in python can lead to major headaches in data preprocessing. That’s why companies expect you to master this first.
Small Errors That Cost Big in Python
- Assigning a mutable object as a default argument in a function.
- Trying to change an element in a tuple.
- Using a string method on a list.
These things seem small but often cause major runtime errors. So be cautious, especially when handling immutable data types in python.
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FAQs
1. What is the role of data types in a data science course?
Understanding Python data types is foundational in any data science course, especially when working with datasets, transformations, and type-safe operations.
2. How do Python data types affect memory usage?
Mutable types can use more memory if altered frequently. Knowing when to use immutable types helps optimise performance.
3. Which data types in python are immutable?
Immutable data types in python include int, float, str, and tuple. You can not change their values after the assignment.
4. Which data types in python are mutable?
The list, dictionary, and set are mutable. You can easily modify their content once you create them.
5. Can you convert one Python data type into another?
Yes, using type casting like int(), str(), or list(), you can convert between compatible data types in python.
6. Is a string mutable or immutable in python?
A string is immutable. You cannot change a specific character in a string.
7. Why do I get TypeError in Python?
TypeErrors usually happen when you try to perform an operation not supported by the data type used, like adding a string to an integer.