Python Data Types

An introduction to data types in Python.

3 min readupdated 6 Oct 2026

Understanding Data Types in Python

Data types in Python are a fundamental concept that plays a crucial role in defining the nature of variables and how they behave during operations. Python is a dynamically-typed language, which means that the interpreter can determine the type of a variable at runtime. This flexibility allows for more concise and expressive code. Let's delve into the essential data types in Python.

Diagram:

diagram Python built-in data types mermaid

  graph TD
    A[Python Data Types] --> B[Numeric]
    A --> C[Sequence]
    A --> D[Set]
    A --> E[Mapping]
    A --> F[Boolean]
    A --> G[None]
    B --> B1[int]
    B --> B2[float]
    B --> B3[complex]
    C --> C1[str]
    C --> C2[list]
    C --> C3[tuple]
    D --> D1[set]
    D --> D2[frozenset]
    E --> E1[dict]

The main categories of built-in types

Dynamic Typing

Because Python infers types at runtime, the same variable can hold different types over its life — the type belongs to the value, not the variable.

Python

x = 42          # x is now an int
print(type(x))  # <class 'int'>
x = "hello"     # x is now a str — perfectly legal
print(type(x))  # <class 'str'>

Output:

cmd

C:\Users\Your Name> python dynamic.py
<class 'int'>
<class 'str'>

Numeric Data Types

Integers

`int`

Integers represent whole numbers without any fractional part. They can be positive or negative.

Python

age = 25
population = -1000

or you can use the int() function to convert a string to an integer.

Python

age = int(25)
population = int(-1000)

Floating-Point Numbers

`float`

Floating-point numbers are used to represent real numbers with a decimal point.

Python

pi = 3.14
temperature = -15.5

or you can use the float() function to convert a string to a floating-point number.

Python

pi = float(3.14)
temperature = float(-15.5)

Complex Numbers

`complex`

Complex numbers have both a real and an imaginary part.

Python

z = 3 + 4j

or you can use the complex() function to create a complex number.

Python

z = complex(3, 4)

Sequence Types

Strings

`str`

Strings are sequences of characters and are enclosed in single or double quotes.

Python

name = "John"
message = 'Hello, Python!'

or you can use the str() function to convert a number to a string.

Python

name = str("John")
message = str('Hello, Python!')

Lists

`list`

Lists are ordered, mutable sequences that can contain elements of different data types.

Python

fruits = ['apple', 'banana', 'orange']
mixed_list = [1, 'two', 3.0, [4, 5]]

or you can use the list() function to convert a tuple to a list.

Python

fruits = list(('apple', 'banana', 'orange'))
mixed_list = list((1, 'two', 3.0, [4, 5]))

Tuples

`tuple`

Tuples are ordered, immutable sequences. Once created, their elements cannot be changed.

Python

coordinates = (3, 4)
RGB_color = (255, 0, 0)

or you can use the tuple() function to convert a list to a tuple.

Python

coordinates = tuple([3, 4])
RGB_color = tuple([255, 0, 0])

Set Types

Sets

`set`

Sets are unordered collections of unique elements.

Python

unique_numbers = {1, 2, 3, 4, 5}

or you can use the set() function to create a set.

Python

unique_numbers = set([1, 2, 3, 4, 5])

Mapping Type

Dictionary

`dict`

Dictionaries are unordered collections of key-value pairs.

Python

person = {'name': 'Alice', 'age': 30, 'city': 'Wonderland'}

or you can use the dict() function to create a dictionary.

Python

person = dict({'name': 'Alice', 'age': 30, 'city': 'Wonderland'})

Boolean Type

Boolean

`bool`

Boolean values represent truth or falsehood and are used in logical operations.

Python

is_raining = True
has_pet = False

or you can use the bool() function to convert a number to a boolean.

Python

is_raining = bool(1)
has_pet = bool(0)

Special Types

None Type

`NoneType`

The None type represents the absence of a value or a null value.

Python

result = None

Type Conversion

Python allows you to convert between different data types using built-in functions like int(), float(), str(), etc.

Python

num_str = "42"
num_int = int(num_str)

Checking Data Types

You can check the data type of a variable using the type() function.

Python

age = 25
print(type(age))  # <class 'int'>

Operations on Data Types

Different data types support various operations. For example, you can concatenate strings, perform arithmetic operations on numbers, and use logical operators with booleans.

Python

greeting = "Hello, "
name = "Alice"
full_greeting = greeting + name  # Concatenation of strings

Data Type Table

There are many more data types in Python, but the ones listed above are the most commonly used ones. The following table summarizes the data types in Python.

Data Type	Description	Example
`int`	Integer	`age = 25`
`float`	Floating-point number	`pi = 3.14`
`complex`	Complex number	`z = 3 + 4j`
`str`	String	`name = "Alice"`
`list`	List	`fruits = ['apple', 'banana', 'orange']`
`tuple`	Tuple	`coordinates = (3, 4)`
`set`	Set	`unique_numbers = {1, 2, 3, 4, 5}`
`frozenset`	Frozen set	`unique_numbers = frozenset({1, 2, 3, 4, 5})`
`dict`	Dictionary	`person = {'name': 'Alice', 'age': 30, 'city': 'Wonderland'}`
`bool`	Boolean	`is_raining = True`
`NoneType`	None	`result = None`
`range`	Range	`numbers = range(1, 10)`
`bytes`	Bytes	`data = b'Hello, Python!'`
`bytearray`	Byte array	`data = bytearray(10)`
`memoryview`	Memory view	`data = memoryview(bytes(10))`

Mutable vs. Immutable Types

A crucial distinction: some objects can be changed after creation (mutable), and some cannot (immutable). Trying to change an immutable object creates a new object instead.

Mutable (can change)	Immutable (cannot change)
`list`, `dict`, `set`, `bytearray`	`int`, `float`, `complex`, `bool`
	`str`, `tuple`, `frozenset`, `bytes`

Python

# Lists are mutable - same object is modified
numbers = [1, 2, 3]
numbers[0] = 99
print(numbers)        # [99, 2, 3]
 
# Strings are immutable - this raises an error
text = "hello"
# text[0] = "H"       # TypeError: 'str' object does not support item assignment

Visualize it

Whether a type can be changed in place is one of the most important things to know about it.

diagram Built-in types grouped by mutability mermaid

  flowchart TB
  A["Built-in types"] --> B["Immutable"]
  A --> C["Mutable"]
  B --> B1["int"]
  B --> B2["float"]
  B --> B3["str"]
  B --> B4["bool"]
  B --> B5["tuple"]
  B --> B6["frozenset"]
  C --> C1["list"]
  C --> C2["dict"]
  C --> C3["set"]

Immutable types cannot be changed in place; mutable types can.

Conclusion

Understanding Python's data types is fundamental to writing effective and efficient code. Python's flexibility in handling various data types makes it suitable for a wide range of applications, from simple scripts to complex data analysis and machine learning tasks. As you continue your journey in Python programming, a solid grasp of data types will empower you to manipulate and process data effectively.

Explore more Python concepts and practical examples in our tutorials on Python Central Hub!

Try it: Data Types Exercises

Exercise 1 – Identify Data Types

exerciseYour turn

script.py

# Task: Exercise 1 – Identify Data Types
# Follow the steps below and fill in the blanks.
# Use the 'Show Solution' button only after you have tried!

# Check the type of each variable
# Hint: print(value) displays output; print(a, b) prints multiple values.
# Step: Call print() with the right argument(s).
# Example structure: print(type(42))
print(___)  # replace ___ with the correct expression
# Hint: Use type() to check the data type of a variable, e.g. print(type(42))
# Step: Call print() with the right argument(s).
# Example structure: print(type(3.14))
print(___)  # replace ___ with the correct expression
# Hint: Run after every change -- small steps are easier to debug!
# Step: Call print() with the right argument(s).
# Example structure: print(type("Hello"))
print(___)  # replace ___ with the correct expression
# Hint: Read error messages carefully -- they point to the exact line.
# Step: Call print() with the right argument(s).
# Example structure: print(type(True))
print(___)  # replace ___ with the correct expression
# Step: Call print() with the right argument(s).
# Example structure: print(type([1, 2, 3]))
print(___)  # replace ___ with the correct expression

# ── Expected Output ──────────────────────────────────────────
# (Run your completed code to check the output)
# ──────────────────────────────────────────────────────────────

Output

Press Run to see what your code prints.

Exercise 2 – Working with Different Types

exerciseYour turn

script.py

# Task: Exercise 2 – Working with Different Types
# Follow the steps below and fill in the blanks.
# Use the 'Show Solution' button only after you have tried!

# Hint: print(value) displays output; print(a, b) prints multiple values.
# Step: Assign the correct value (a string) to 'name'.
name = ___  # replace ___ with the correct value
# Step: Assign the correct value (a number) to 'age'.
age = ___  # replace ___ with the correct value
# Hint: Create variables of different types and print them with their types.
# Step: Assign the correct value (a number) to 'height'.
height = ___  # replace ___ with the correct value
# Step: Assign the correct value to 'is_student'.
is_student = ___  # replace ___ with the correct value

# Hint: Run after every change -- small steps are easier to debug!
# Step: Call print() with the right argument(s).
# Example structure: print(name, type(name))
print(___)  # replace ___ with the correct expression
# Step: Call print() with the right argument(s).
# Example structure: print(age, type(age))
print(___)  # replace ___ with the correct expression
# Hint: Read error messages carefully -- they point to the exact line.
# Step: Call print() with the right argument(s).
# Example structure: print(height, type(height))
print(___)  # replace ___ with the correct expression
# Step: Call print() with the right argument(s).
# Example structure: print(is_student, type(is_student))
print(___)  # replace ___ with the correct expression

Output

Press Run to see what your code prints.

Exercise 3 – Collections

exerciseYour turn

script.py

# Task: Exercise 3 – Collections
# Follow the steps below and fill in the blanks.
# Use the 'Show Solution' button only after you have tried!

# Hint: print(value) displays output; print(a, b) prints multiple values.
# Step: Assign the correct value (a list) to 'my_list'.
my_list = ___  # replace ___ with the correct value
# Hint: Create a list, tuple, and dictionary and print their types.
# Step: Assign the correct value (a tuple or function call) to 'my_tuple'.
my_tuple = ___  # replace ___ with the correct value
# Hint: Run after every change -- small steps are easier to debug!
# Step: Assign the correct value (a dict or set) to 'my_dict'.
my_dict = ___  # replace ___ with the correct value

# Hint: Read error messages carefully -- they point to the exact line.
# Step: Call print() with the right argument(s).
# Example structure: print(type(my_list))
print(___)  # replace ___ with the correct expression
# Step: Call print() with the right argument(s).
# Example structure: print(type(my_tuple))
print(___)  # replace ___ with the correct expression
# Step: Call print() with the right argument(s).
# Example structure: print(type(my_dict))
print(___)  # replace ___ with the correct expression

Output

Press Run to see what your code prints.

Python Data Types

Understanding Data Types in Python

Dynamic Typing

Numeric Data Types

Integers

int

Floating-Point Numbers

float

Complex Numbers

complex

Sequence Types

Strings

str

Lists

list

Tuples

tuple

Set Types

Sets

set

Mapping Type

Dictionary

dict

Boolean Type

Boolean

bool

Special Types

None Type

NoneType

Type Conversion

Checking Data Types

Operations on Data Types

Data Type Table

Mutable vs. Immutable Types

Visualize it

Conclusion

Try it: Data Types Exercises

Exercise 1 – Identify Data Types

Exercise 2 – Working with Different Types

Exercise 3 – Collections

Was this page helpful?

`int`

`float`

`complex`

`str`

`list`

`tuple`

`set`

`dict`

`bool`

`NoneType`