Data Types Overview

What Are Data Types?

A data type is a category of data that tells Python what kind of information is being stored and what operations can be performed on it. Think of data types like different containers — a box for books, a jar for cookies, a bottle for water. Each container is designed for a specific purpose.

Python has several built-in data types. Understanding them is the foundation of all Python programming. Let's explore them one by one.

1️⃣ Text Type — String (str)

A string is used for storing text and words. Strings are created by enclosing text in single quotes, double quotes, or triple quotes.

name = "Alice"
message = 'Hello, World!'
poem = """This is a
multiline string
across three lines"""

print(name)     # Output: Alice
print(message)  # Output: Hello, World!

2️⃣ Numeric Types — Integers and Floats

Python has two main numeric types for storing numbers: integers and floats.

Integers (int) — Whole Numbers

Integers are whole numbers without decimal points. They can be positive, negative, or zero, and Python allows them to be as large as you need.

age = 25
score = -10
count = 0
big_number = 999999999999999

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

Floats (float) — Decimal Numbers

Floats are numbers with decimal points. Use them when you need precision beyond whole numbers.

height = 5.9
price = 19.99
temperature = -0.5

print(height)        # Output: 5.9
print(type(price))  # Output: <class 'float'>

Complex Numbers (complex) — Advanced

Complex numbers have a real part and an imaginary part. They're used in advanced mathematics and scientific computing. For beginners, you can skip this for now.

z = 2 + 3j
print(z)        # Output: (2+3j)
print(type(z))  # Output: <class 'complex'>

3️⃣ Sequence Types — Lists, Tuples, and Ranges

Sequence types are collections that store multiple items in a specific order. Let's explore each one.

Lists (list) — Ordered and Changeable

A list is an ordered collection of items that CAN be changed. Lists are enclosed in square brackets, and items are separated by commas.

fruits = ["apple", "banana", "orange"]
grades = [95, 87, 92]
mixed = [1, "hello", 3.14]  # can mix types

print(fruits)        # Output: ['apple', 'banana', 'orange']
print(fruits[0])     # Output: apple (first item)
print(type(fruits))  # Output: <class 'list'>

Tuples (tuple) — Ordered and Unchangeable

A tuple is like a list, but CANNOT be changed after creation. Tuples are enclosed in parentheses and are useful when you want data to stay constant.

coordinates = (10, 20)
rgb_color = (255, 0, 0)  # red color
single_item = (42,)  # note the comma for single item

print(coordinates)        # Output: (10, 20)
print(coordinates[0])     # Output: 10
print(type(coordinates))  # Output: <class 'tuple'>

Range (range) — Sequence of Numbers

A range is a sequence of numbers, commonly used with loops. It's created using the range() function.

numbers = range(5)  # generates 0, 1, 2, 3, 4

for i in range(3):
    print(i)

# Output:
# 0
# 1
# 2

4️⃣ Mapping Type — Dictionary (dict)

A dictionary stores data as key-value pairs. It's like a real dictionary where you look up a word and get its definition. Dictionaries are enclosed in curly braces.

person = {
    "name": "Bob",
    "age": 30,
    "city": "NYC",
    "is_student": False
}

print(person)           # Output: all key-value pairs
print(person["name"])    # Output: Bob
print(person["age"])     # Output: 30
print(type(person))    # Output: <class 'dict'>

5️⃣ Set Types — Sets and Frozensets

Sets are unordered collections of unique items. If you add duplicate items, they are automatically removed.

Sets (set) — Unordered and Unique

colors = {"red", "green", "blue"}
numbers = {1, 2, 2, 3}  # duplicate 2 removed

print(colors)        # Output: {'red', 'green', 'blue'} (order may vary)
print(numbers)      # Output: {1, 2, 3}
print(type(numbers))  # Output: <class 'set'>

Frozensets (frozenset) — Immutable Sets

Frozensets are like sets, but immutable — they cannot be changed after creation.

frozen = frozenset([1, 2, 3])
print(frozen)        # Output: frozenset({1, 2, 3})
print(type(frozen))  # Output: <class 'frozenset'>

6️⃣ Boolean Type — True or False

A boolean represents one of two values: True or False. Booleans are used extensively in decision-making and comparisons.

is_student = True
is_raining = False

print(is_student)        # Output: True
print(type(is_student))  # Output: <class 'bool'>

# Booleans from comparisons
result = 5 > 3  # Evaluates to True
print(result)  # Output: True

7️⃣ Special Type — None

The None type represents the absence of a value. It's used when a variable has no value or when a function returns nothing.

result = None

print(result)        # Output: None
print(type(result))  # Output: <class 'NoneType'>

8️⃣ Binary Types — Bytes and Bytearrays

Binary types are used for working with raw byte data. They are less common for beginners but important for advanced work.

data = b"Hello"  # bytes
arr = bytearray(b"Hello")  # bytearray

print(data)        # Output: b'Hello'
print(type(data))  # Output: <class 'bytes'>
print(type(arr))   # Output: <class 'bytearray'>

Mutable vs. Immutable — Important Concept

Mutable means the data can be changed after creation. Immutable means it cannot be changed.

Type Conversion — Converting Between Types

Sometimes you need to convert one data type to another. Python provides functions to do this.

# Convert string to integer
age_str = "25"
age_int = int(age_str)  # becomes 25
print(age_int)  # Output: 25

# Convert integer to string
number = 42
text = str(number)  # becomes "42"
print(text)  # Output: 42

# Convert to float
price = float("19.99")
print(price)  # Output: 19.99

Quick Reference — Data Types Summary Table