Data Types in Python

In Python, every value has a specific data type. A data type tells Python what kind of value it is storing and how that value should behave.

Data types are a foundation topic. Once you understand them, writing Python becomes easier because you know what each kind of data can do.

What Data Types Control

• What operations are allowed (example: you can add numbers, but you cannot add a number to a list).
• How values are stored in memory.
• How Python interprets your code during execution.
• How data behaves inside conditions, loops, and functions.

Checking a Value’s Data Type

Python provides a built-in function called type() to check the data type of a value.

# Checking types using type()

a = 50
b = 12.9
c = "Hello"

print(type(a))
print(type(b))
print(type(c))

<class 'int'>
<class 'float'>
<class 'str'>

• int means integer (whole number).
• float means decimal number.
• str means string (text).

Numeric Data Types

Python supports multiple numeric types. The most common are:

• int for whole numbers
• float for decimal numbers
• complex for scientific use (less common for beginners)

# Numeric types

x = 10          # int (whole number)
y = 12.75       # float (decimal number)
z = 2 + 5j      # complex (advanced numeric type)

print(x)
print(y)
print(z)

print(type(x))
print(type(y))
print(type(z))

10
12.75
(2+5j)
<class 'int'>
<class 'float'>
<class 'complex'>

• Use int for counts, ages, quantities.
• Use float for prices, measurements, percentages.
• Complex numbers are mainly used in advanced math and science.

String Data Type (str)

Strings represent text. They must be written inside single quotes or double quotes.

# Strings (text values)

platform = "Dataplexa"
topic = "Python"

print(platform)
print(topic)

print(platform.upper())   # convert to uppercase
print(len(topic))         # count characters

Dataplexa
Python
DATAPLEXA
6

• upper() converts text to uppercase.
• len() gives the length of a string.
• Strings are used everywhere: names, messages, labels, file paths.

Boolean Data Type (bool)

A boolean represents a truth value: True or False. Booleans are heavily used in conditions and decision-making logic.

# Boolean values and comparisons

is_logged_in = True
has_access = False

print(is_logged_in)
print(has_access)

print(5 > 3)     # comparison returns True
print(10 < 2)    # comparison returns False

True
False
True
False

• Comparisons like > and < return booleans.
• Booleans help programs choose different paths using if/else.

List Data Type (list)

A list stores multiple values in a single variable. Lists are ordered and mutable (you can change them).

# Lists store multiple items

fruits = ["apple", "banana", "mango"]

print(fruits)
print(fruits[1])          # second item (index starts at 0)

fruits.append("orange")   # add a new item
print(fruits)

['apple', 'banana', 'mango']
banana
['apple', 'banana', 'mango', 'orange']

• Lists keep the order of elements.
• Indexing starts at 0, so fruits[1] is the second element.
• append() adds an item to the end of the list.

Tuple Data Type (tuple)

A tuple is similar to a list, but it is immutable (cannot be changed). Tuples are used when values should stay constant.

# Tuples are fixed collections

coordinates = (10, 20)

print(coordinates)
print(coordinates[0])     # first item

(10, 20)
10

• Tuples use parentheses ().
• You can read values, but you should not modify them.
• Common usage: coordinates, fixed settings, constant pairs.

Dictionary Data Type (dict)

A dictionary stores data as key-value pairs. You use keys to quickly access the related values.

# Dictionaries store key-value pairs

student = {
    "name": "Alex",
    "age": 21,
    "course": "Python"
}

print(student["name"])
print(student["course"])

Alex
Python

• Keys like "name" and "course" are used to access values.
• Dictionaries are common in APIs, JSON, settings, and structured data.

Set Data Type (set)

A set stores unique values. Duplicate values are automatically removed.

# Sets store unique values

nums = {10, 20, 30, 20, 10}

print(nums)
print(20 in nums)
print(99 in nums)

{10, 20, 30}
True
False

• Duplicates are removed automatically.
• Sets are useful for membership checks like value in set.
• The order of set output may vary because sets are unordered.

Real-World Example: Order Summary Using Multiple Data Types

Real programs combine multiple data types. This example uses string, float, int, bool, and a calculated total.

# Order information (different data types)

item = "Bluetooth Speaker"   # str (text)
price = 1499.99              # float (decimal)
quantity = 2                 # int (count)
in_stock = True              # bool (true/false)

# Total price calculation
total = price * quantity

# Output
print("Item:", item)
print("Quantity:", quantity)
print("In Stock:", in_stock)
print("Total Price:", total)

Item: Bluetooth Speaker
Quantity: 2
In Stock: True
Total Price: 2999.98

Why This Example Matters

• item is text, because product names are strings.
• price is decimal, so it uses float.
• quantity is a count, so it uses int.
• in_stock is a True/False state, so it uses bool.
• total is computed using numeric values.

Practice

Quick Quiz

Data Types Summary Table

Recap: You learned the major Python data types, how they behave, how to check types using type(), and how different types work together in real programs.

Next up: Type Conversion and Type Casting.